on-policy-self-distillation
claude-opus-5
on-policy-self-distillation — graded by the task's verifier against a measured baseline.
- backend
- modal
- variant
- claude claude-opus-5
- metric
- AIME24 avg@12 accuracy
- actions
- 148
- duration
- 8h 50m
- recorded
- 2026-08-03
run idopsd-aime24-4gpus__g75tHnA
Everything claude-opus-5 did on this task, in order.
download JSON ↗claude-codeclaude-opus-5138 turns34 narration148 action148 observation104 reasoning1 final
#26
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolEdit
args
{
"replace_all": false,
"file_path": "/workspace/repo/opsd_trainer.py",
"old_string": " # Create labels for generation tokens\n # Mask prompt tokens (use per-example lengths for accurate masking)\n labels = generated_ids.clone()\n for i in range(labels.shape[0]):\n actual_prompt_len = inputs[\"student_prompt_lengths_per_example\"][i].item()\n labels[i, :actual_prompt_len] = -100 # Mask actual prompt",
"new_string": " # Create labels for generation tokens.\n # Prompts are LEFT padded, so the whole prompt block (pads + prompt) is columns\n # [0, student_prompt_len) for every example; mask it wholesale.\n labels = generated_ids.clone()\n labels[:, :student_prompt_len] = -100"
}Edit result
The file /workspace/repo/opsd_trainer.py has been updated successfully. (file state is current in your context — no need to Read it back)
[metadata] {"filePath": "/workspace/repo/opsd_trainer.py", "oldString": " # Create labels for generation tokens\n # Mask prompt tokens (use per-example lengths for accurate masking)\n labels = generated_ids.clone()\n for i in range(labels.shape[0]):\n actual_prompt_len = inputs[\"student_prompt_lengths_per_example\"][i].item()\n labels[i, :actual_prompt_len] = -100 # Mask actual prompt", "newString": " # Create labels for generation tokens.\n # Prompts are LEFT padded, so the whole prompt block (pads + prompt) is columns\n # [0, student_prompt_len) for every example; mask it wholesale.\n labels = generated_ids.clone()\n labels[:, :student_prompt_len] = -100", "originalFile": null, "structuredPatch": [{"oldStart": 1414, "oldLines": 12, "newStart": 1414, "newLines": 11, "lines": [" inputs[\"teacher_input_ids\"] = teacher_full_ids", " inputs[\"teacher_attention_mask\"] = teacher_attention_mask", " ", "- # Create labels for generation tokens", "- # Mask prompt tokens (use per-example lengths for accurate masking)", "+ # Create labels for generation tokens.", "+ # Prompts are LEFT padded, so the whole prompt block (pads + prompt) is columns", "+ # [0, student_prompt_len) for every example; mask it wholesale.", " labels = generated_ids.clone()", "- for i in range(labels.shape[0]):", "- actual_prompt_len = inputs[\"student_prompt_lengths_per_example\"][i].item()", "- labels[i, :actual_prompt_len] = -100 # Mask actual prompt", "+ labels[:, :student_prompt_len] = -100", " ", " if self.processing_class.pad_token_id is not None:", " labels[labels == self.processing_class.pad_token_id] = -100"]}], "userModified": false, "replaceAll": false}#27
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashAudit padding assumptions
args
{
"command": "python - <<'EOF'\nimport re\nfor f in ['opsd_trainer.py','data_collator.py','opsd_train.py']:\n t=open(f).read()\n for i,l in enumerate(t.split('\\n'),1):\n if 'padding_side' in l or 'lengths_per_example' in l or 'student_prompt_length' in l or 'teacher_prompt_length' in l:\n print(f\"{f}:{i}: {l.strip()}\")\nEOF",
"description": "Audit padding assumptions"
}Bash result
opsd_trainer.py:633: student_prompt_len = inputs["student_prompt_length"]
opsd_trainer.py:634: teacher_prompt_len = inputs["teacher_prompt_length"]
opsd_trainer.py:1139: completion_ids_tensors, padding_value=self.processing_class.pad_token_id, padding_side="right"
opsd_trainer.py:1362: inputs["teacher_prompt_length"] = teacher_prompts_with_reasoning.shape[1]
opsd_trainer.py:1381: student_prompt_len = inputs["student_prompt_length"]
opsd_trainer.py:1394: inputs["student_prompt_length"] = student_prompt_len
opsd_trainer.py:1396: student_prompt_len = inputs["student_prompt_length"]
data_collator.py:57: print(f"[DataCollator] Original padding_side: {self.tokenizer.padding_side}")
data_collator.py:58: self.tokenizer.padding_side = "left"
data_collator.py:59: print(f"[DataCollator] Set padding_side to: {self.tokenizer.padding_side}")
data_collator.py:130: student_prompt_lengths = [len(ids) for ids in student_encoded_no_pad["input_ids"]]
data_collator.py:133: max_student_prompt_len = max(student_prompt_lengths)
data_collator.py:147: "student_prompt_length": max_student_prompt_len, # Single value for batch!
data_collator.py:149: "student_prompt_lengths_per_example": torch.tensor(student_prompt_lengths),
data_collator.py:197: teacher_prompt_lengths = [len(ids) for ids in teacher_encoded_no_pad["input_ids"]]
data_collator.py:198: max_teacher_prompt_len = max(teacher_prompt_lengths)
data_collator.py:212: "teacher_prompt_length": max_teacher_prompt_len,
data_collator.py:213: "teacher_prompt_lengths_per_example": torch.tensor(teacher_prompt_lengths),
opsd_train.py:251: padding_side="left",
[stdout]
opsd_trainer.py:633: student_prompt_len = inputs["student_prompt_length"]
opsd_trainer.py:634: teacher_prompt_len = inputs["teacher_prompt_length"]
opsd_trainer.py:1139: completion_ids_tensors, padding_value=self.processing_class.pad_token_id, padding_side="right"
opsd_trainer.py:1362: inputs["teacher_prompt_length"] = teacher_prompts_with_reasoning.shape[1]
opsd_trainer.py:1381: student_prompt_len = inputs["student_prompt_length"]
opsd_trainer.py:1394: inputs["student_prompt_length"] = student_prompt_len
opsd_trainer.py:1396: student_prompt_len = inputs["student_prompt_length"]
data_collator.py:57: print(f"[DataCollator] Original padding_side: {self.tokenizer.padding_side}")
data_collator.py:58: self.tokenizer.padding_side = "left"
data_collator.py:59: print(f"[DataCollator] Set padding_side to: {self.tokenizer.padding_side}")
data_collator.py:130: student_prompt_lengths = [len(ids) for ids in student_encoded_no_pad["input_ids"]]
data_collator.py:133: max_student_prompt_len = max(student_prompt_lengths)
data_collator.py:147: "student_prompt_length": max_student_prompt_len, # Single value for batch!
data_collator.py:149: "student_prompt_lengths_per_example": torch.tensor(student_prompt_lengths),
data_collator.py:197: teacher_prompt_lengths = [len(ids) for ids in teacher_encoded_no_pad["input_ids"]]
data_collator.py:198: max_teacher_prompt_len = max(teacher_prompt_lengths)
data_collator.py:212: "teacher_prompt_length": max_teacher_prompt_len,
data_collator.py:213: "teacher_prompt_lengths_per_example": torch.tensor(teacher_prompt_lengths),
opsd_train.py:251: padding_side="left",
[metadata] {"noOutputExpected": false}#28
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashCreate 4-step smoke test script
args
{
"command": "cd /workspace && sed -e 's|--max_steps 100|--max_steps 4|' -e 's|--save_steps 100|--save_steps 1000|' -e 's|--logging_steps 2|--logging_steps 1|' train_capped.sh > /workspace/smoke.sh && python - <<'EOF'\nt=open('/workspace/smoke.sh').read()\nassert '--max_steps 4' in t and '--save_steps 1000' in t\n# drop the checkpoint-100 assertion at the end\nt=t.split('CKPT=\"$OUT_DIR')[0]+'exit $rc\\n'\nopen('/workspace/smoke.sh','w').write(t)\nprint(\"ok\")\nEOF",
"description": "Create 4-step smoke test script"
}Bash result
ok
[stdout]
ok
[metadata] {"noOutputExpected": false}#29
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashSmoke test the padding fix
args
{
"command": "SEED=42 OUT_DIR=/workspace/runs/smokeA PORT=12961 bash /workspace/smoke.sh > /workspace/smokeA.out 2>&1; echo \"RC=$?\"",
"description": "Smoke test the padding fix",
"run_in_background": true
}Bash result
Command running in background with ID: bkie3p0pz. Output is being written to: /tmp/claude-0/-workspace/5fd2f3dc-5928-49f9-b3bb-a6ebc92f09aa/tasks/bkie3p0pz.output. You will be notified when it completes. To check interim output, use Read on that file path.
[metadata] {"noOutputExpected": false, "backgroundTaskId": "bkie3p0pz"}#30
narration
Now implementing candidate B — replacing per-entry hard clipping with a direction-preserving per-token gradient cap.
toolEdit
args
{
"replace_all": false,
"file_path": "/workspace/repo/opsd_trainer.py",
"old_string": " logits_are_probs=False,\n top_k=None,\n token_clip=None,\n ):",
"new_string": " logits_are_probs=False,\n top_k=None,\n token_clip=None,\n clip_mode=\"entry\",\n ):"
}Edit result
The file /workspace/repo/opsd_trainer.py has been updated successfully. (file state is current in your context — no need to Read it back)
[metadata] {"filePath": "/workspace/repo/opsd_trainer.py", "oldString": " logits_are_probs=False,\n top_k=None,\n token_clip=None,\n ):", "newString": " logits_are_probs=False,\n top_k=None,\n token_clip=None,\n clip_mode=\"entry\",\n ):", "originalFile": null, "structuredPatch": [{"oldStart": 389, "oldLines": 6, "newStart": 389, "newLines": 7, "lines": [" logits_are_probs=False,", " top_k=None,", " token_clip=None,", "+ clip_mode=\"entry\",", " ):", " \"\"\"", " Compute the generalized Jensen-Shannon Divergence loss for knowledge distillation using F.kl_div. See Eq. (1)"]}], "userModified": false, "replaceAll": false}#31
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolEdit
args
{
"replace_all": false,
"file_path": "/workspace/repo/opsd_trainer.py",
"old_string": " # Per-token clipping: cap each token's divergence value\n if token_clip is not None:\n jsd = jsd.clamp(max=token_clip)\n\n # Masking\n if labels is not None:\n mask = labels != -100\n jsd = jsd[mask]",
"new_string": " if clip_mode == \"token\":\n # Direction-preserving per-token capping.\n #\n # `entry` mode (the released recipe) clamps every *vocabulary entry* at tau. An\n # entry exceeds tau exactly when the teacher puts real mass on a token the student\n # under-weights -- i.e. precisely the entries carrying the teacher's correction --\n # and clamping zeroes their gradient. Measured on Qwen3-1.7B, the teacher's own\n # argmax entry is clamped at ~11% of positions, and on high-divergence positions\n # 50-80% of the teacher's probability mass is clamped away. The surviving gradient\n # there points at the renormalized *tail* of the teacher, so the most informative\n # positions are the ones whose target gets corrupted.\n #\n # Instead, keep the full-vocabulary divergence direction at every position and cap\n # only its magnitude: scale each position by a detached factor tau/k_n when its\n # divergence k_n exceeds tau. The loss value still equals min(k_n, tau), so the\n # heavy tail of stylistic tokens can no longer dominate the batch -- which is the\n # stated purpose of the clip -- but the gradient at every position remains the true\n # forward-KL direction, just norm-limited.\n per_token = jsd.sum(-1)\n if token_clip is not None:\n scale = (token_clip / per_token.detach().clamp_min(1e-6)).clamp(max=1.0)\n per_token = per_token * scale\n jsd = per_token\n elif token_clip is not None:\n # Per-entry clipping: cap each vocabulary entry's divergence contribution\n jsd = jsd.clamp(max=token_clip)\n\n # Masking\n if labels is not None:\n mask = labels != -100\n jsd = jsd[mask]"
}Edit result
The file /workspace/repo/opsd_trainer.py has been updated successfully. (file state is current in your context — no need to Read it back)
[metadata] {"filePath": "/workspace/repo/opsd_trainer.py", "oldString": " # Per-token clipping: cap each token's divergence value\n if token_clip is not None:\n jsd = jsd.clamp(max=token_clip)\n\n # Masking\n if labels is not None:\n mask = labels != -100\n jsd = jsd[mask]", "newString": " if clip_mode == \"token\":\n # Direction-preserving per-token capping.\n #\n # `entry` mode (the released recipe) clamps every *vocabulary entry* at tau. An\n # entry exceeds tau exactly when the teacher puts real mass on a token the student\n # under-weights -- i.e. precisely the entries carrying the teacher's correction --\n # and clamping zeroes their gradient. Measured on Qwen3-1.7B, the teacher's own\n # argmax entry is clamped at ~11% of positions, and on high-divergence positions\n # 50-80% of the teacher's probability mass is clamped away. The surviving gradient\n # there points at the renormalized *tail* of the teacher, so the most informative\n # positions are the ones whose target gets corrupted.\n #\n # Instead, keep the full-vocabulary divergence direction at every position and cap\n # only its magnitude: scale each position by a detached factor tau/k_n when its\n # divergence k_n exceeds tau. The loss value still equals min(k_n, tau), so the\n # heavy tail of stylistic tokens can no longer dominate the batch -- which is the\n # stated purpose of the clip -- but the gradient at every position remains the true\n # forward-KL direction, just norm-limited.\n per_token = jsd.sum(-1)\n if token_clip is not None:\n scale = (token_clip / per_token.detach().clamp_min(1e-6)).clamp(max=1.0)\n per_token = per_token * scale\n jsd = per_token\n elif token_clip is not None:\n # Per-entry clipping: cap each vocabulary entry's divergence contribution\n jsd = jsd.clamp(max=token_clip)\n\n # Masking\n if labels is not None:\n mask = labels != -100\n jsd = jsd[mask]", "originalFile": null, "structuredPatch": [{"oldStart": 460, "oldLines": 8, "newStart": 460, "newLines": 31, "lines": [" # Compute the Generalized Jensen-Shannon Divergence", " jsd = beta * kl_teacher + (1 - beta) * kl_student", " ", "- # Per-token clipping: cap each token's divergence value", "- if token_clip is not None:", "+ if clip_mode == \"token\":", "+ # Direction-preserving per-token capping.", "+ #", "+ # `entry` mode (the released recipe) clamps every *vocabulary entry* at tau. An", "+ # entry exceeds tau exactly when the teacher puts real mass on a token the student", "+ # under-weights -- i.e. precisely the entries carrying the teacher's correction --", "+ # and clamping zeroes their gradient. Measured on Qwen3-1.7B, the teacher's own", "+ # argmax entry is clamped at ~11% of positions, and on high-divergence positions", "+ # 50-80% of the teacher's probability mass is clamped away. The surviving gradient", "+ # there points at the renormalized *tail* of the teacher, so the most informative", "+ # positions are the ones whose target gets corrupted.", "+ #", "+ # Instead, keep the full-vocabulary divergence direction at every position and cap", "+ # only its magnitude: scale each position by a detached factor tau/k_n when its", "+ # divergence k_n exceeds tau. The loss value still equals min(k_n, tau), so the", "+ # heavy tail of stylistic tokens can no longer dominate the batch -- which is the", "+ # stated purpose of the clip -- but the gradient at every position remains the true", "+ # forward-KL direction, just norm-limited.", "+ per_token = jsd.sum(-1)", "+ if token_clip is not None:", "+ scale = (token_clip / per_token.detach().clamp_min(1e-6)).clamp(max=1.0)", "+ per_token = per_token * scale", "+ jsd = per_token", "+ elif token_clip is not None:", "+ # Per-entry clipping: cap each vocabulary entry's divergence contribution", " jsd = jsd.clamp(max=token_clip)", " ", " # Masking"]}], "userModified": false, "replaceAll": false}#32
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolEdit
args
{
"replace_all": false,
"file_path": "/workspace/repo/opsd_trainer.py",
"old_string": " self.top_k_loss = top_k_loss\n self.jsd_token_clip = jsd_token_clip",
"new_string": " self.top_k_loss = top_k_loss\n self.jsd_token_clip = jsd_token_clip\n # \"entry\" = released recipe (clamp each vocabulary entry at tau).\n # \"token\" = clamp the magnitude of each position's full-vocabulary divergence instead,\n # preserving its direction. See `generalized_jsd_loss`.\n self.jsd_clip_mode = os.environ.get(\"OPSD_CLIP_MODE\", \"entry\")\n if self.jsd_clip_mode not in (\"entry\", \"token\"):\n raise ValueError(f\"OPSD_CLIP_MODE must be 'entry' or 'token', got {self.jsd_clip_mode}\")\n print(f\"[OPSD] jsd clip mode: {self.jsd_clip_mode} (tau={self.jsd_token_clip})\")"
}Edit result
The file /workspace/repo/opsd_trainer.py has been updated successfully. (file state is current in your context — no need to Read it back)
[metadata] {"filePath": "/workspace/repo/opsd_trainer.py", "oldString": " self.top_k_loss = top_k_loss\n self.jsd_token_clip = jsd_token_clip", "newString": " self.top_k_loss = top_k_loss\n self.jsd_token_clip = jsd_token_clip\n # \"entry\" = released recipe (clamp each vocabulary entry at tau).\n # \"token\" = clamp the magnitude of each position's full-vocabulary divergence instead,\n # preserving its direction. See `generalized_jsd_loss`.\n self.jsd_clip_mode = os.environ.get(\"OPSD_CLIP_MODE\", \"entry\")\n if self.jsd_clip_mode not in (\"entry\", \"token\"):\n raise ValueError(f\"OPSD_CLIP_MODE must be 'entry' or 'token', got {self.jsd_clip_mode}\")\n print(f\"[OPSD] jsd clip mode: {self.jsd_clip_mode} (tau={self.jsd_token_clip})\")", "originalFile": null, "structuredPatch": [{"oldStart": 188, "oldLines": 6, "newStart": 188, "newLines": 13, "lines": [" self.reason_first = reason_first", " self.top_k_loss = top_k_loss", " self.jsd_token_clip = jsd_token_clip", "+ # \"entry\" = released recipe (clamp each vocabulary entry at tau).", "+ # \"token\" = clamp the magnitude of each position's full-vocabulary divergence instead,", "+ # preserving its direction. See `generalized_jsd_loss`.", "+ self.jsd_clip_mode = os.environ.get(\"OPSD_CLIP_MODE\", \"entry\")", "+ if self.jsd_clip_mode not in (\"entry\", \"token\"):", "+ raise ValueError(f\"OPSD_CLIP_MODE must be 'entry' or 'token', got {self.jsd_clip_mode}\")", "+ print(f\"[OPSD] jsd clip mode: {self.jsd_clip_mode} (tau={self.jsd_token_clip})\")", " self.use_ema_teacher = use_ema_teacher", " self.ema_decay = ema_decay", " self._ema_params = None # lazily initialized on first optimizer step"]}], "userModified": false, "replaceAll": false}#33
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolEdit
args
{
"replace_all": false,
"file_path": "/workspace/repo/opsd_trainer.py",
"old_string": " top_k=self.top_k_loss,\n token_clip=self.jsd_token_clip,\n )",
"new_string": " top_k=self.top_k_loss,\n token_clip=self.jsd_token_clip,\n clip_mode=self.jsd_clip_mode,\n )"
}Edit result
The file /workspace/repo/opsd_trainer.py has been updated successfully. (file state is current in your context — no need to Read it back)
[metadata] {"filePath": "/workspace/repo/opsd_trainer.py", "oldString": " top_k=self.top_k_loss,\n token_clip=self.jsd_token_clip,\n )", "newString": " top_k=self.top_k_loss,\n token_clip=self.jsd_token_clip,\n clip_mode=self.jsd_clip_mode,\n )", "originalFile": null, "structuredPatch": [{"oldStart": 772, "oldLines": 6, "newStart": 772, "newLines": 7, "lines": [" temperature=self.temperature, # Let the function handle temperature", " top_k=self.top_k_loss,", " token_clip=self.jsd_token_clip,", "+ clip_mode=self.jsd_clip_mode,", " )", " del student_logits_for_loss, teacher_logits_for_loss", " "]}], "userModified": false, "replaceAll": false}#34
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashCheck smoke test A progress
args
{
"command": "tail -25 /workspace/smokeA.out 2>/dev/null; echo \"=== grep loss ===\"; python - <<'EOF'\nimport re\ntry:\n t=open('/workspace/smokeA.out').read()\nexcept: t=''\nfor l in t.split('\\n'):\n if \"'loss'\" in l or 'DataCollator' in l or 'Error' in l or 'Traceback' in l or 'clip mode' in l:\n print(l.strip()[:300])\nEOF",
"description": "Check smoke test A progress"
}Bash result
`torch_dtype` is deprecated! Use `dtype` instead!
Using the latest cached version of the dataset since siyanzhao/Openthoughts_math_30k_opsd couldn't be found on the Hugging Face Hub (offline mode is enabled).
Found the latest cached dataset configuration 'default' at /opt/hf_cache/datasets/siyanzhao___openthoughts_math_30k_opsd/default/0.0.0/1f33e9dc2e8a1c639ca74f8024ad4a9f1f5eae62 (last modified on Sun Aug 2 17:14:36 2026).
Using the latest cached version of the dataset since siyanzhao/Openthoughts_math_30k_opsd couldn't be found on the Hugging Face Hub (offline mode is enabled).
Found the latest cached dataset configuration 'default' at /opt/hf_cache/datasets/siyanzhao___openthoughts_math_30k_opsd/default/0.0.0/1f33e9dc2e8a1c639ca74f8024ad4a9f1f5eae62 (last modified on Sun Aug 2 17:14:36 2026).
Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]Using the latest cached version of the dataset since siyanzhao/Openthoughts_math_30k_opsd couldn't be found on the Hugging Face Hub (offline mode is enabled).
Found the latest cached dataset configuration 'default' at /opt/hf_cache/datasets/siyanzhao___openthoughts_math_30k_opsd/default/0.0.0/1f33e9dc2e8a1c639ca74f8024ad4a9f1f5eae62 (last modified on Sun Aug 2 17:14:36 2026).
[DataCollator] Original padding_side: left
[DataCollator] Set padding_side to: left
[DataCollator] Reason first mode: False
[DataCollator] Original padding_side: left
[DataCollator] Set padding_side to: left
[DataCollator] Reason first mode: False
`torch_dtype` is deprecated! Use `dtype` instead!
`torch_dtype` is deprecated! Use `dtype` instead!
[DataCollator] Original padding_side: left
[DataCollator] Set padding_side to: left
[DataCollator] Reason first mode: False
Loading checkpoint shards: 100%|██████████| 2/2 [00:00<00:00, 36.18it/s]
`torch_dtype` is deprecated! Use `dtype` instead!
Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]
Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]
Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]
Loading checkpoint shards: 100%|██████████| 2/2 [00:00<00:00, 36.35it/s]
Loading checkpoint shards: 100%|██████████| 2/2 [00:00<00:00, 39.76it/s]
Loading checkpoint shards: 100%|██████████| 2/2 [00:00<00:00, 35.95it/s]
Converting train dataset to ChatML: 0%| | 0/29434 [00:00<?, ? examples/s]
Converting train dataset to ChatML: 3%|▎ | 776/29434 [00:00<00:03, 7584.11 examples/s]
Converting train dataset to ChatML: 6%|▌ | 1790/29434 [00:00<00:04, 5596.30 examples/s]
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Converting train dataset to ChatML: 13%|█▎ | 3810/29434 [00:00<00:04, 6089.20 examples/s]
Converting train dataset to ChatML: 16%|█▋ | 4833/29434 [00:00<00:04, 6099.49 examples/s]
Converting train dataset to ChatML: 20%|█▉ | 5806/29434 [00:00<00:03, 5973.05 examples/s]
Converting train dataset to ChatML: 23%|██▎ | 6831/29434 [00:01<00:03, 5892.29 examples/s]
Converting train dataset to ChatML: 26%|██▋ | 7761/29434 [00:01<00:03, 5494.50 examples/s]
Converting train dataset to ChatML: 29%|██▊ | 8404/29434 [00:01<00:04, 5216.24 examples/s]
Converting train dataset to ChatML: 31%|███ | 9000/29434 [00:01<00:04, 5062.67 examples/s]
Converting train dataset to ChatML: 34%|███▎ | 9861/29434 [00:01<00:03, 5857.03 examples/s]
Converting train dataset to ChatML: 37%|███▋ | 10795/29434 [00:01<00:03, 5819.78 examples/s]
Converting train dataset to ChatML: 39%|███▊ | 11400/29434 [00:02<00:03, 5635.67 examples/s]
Converting train dataset to ChatML: 41%|████ | 12000/29434 [00:02<00:03, 5406.21 examples/s]
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Tokenizing train dataset: 14%|█▍ | 4256/29434 [00:32<02:59, 140.47 examples/s]=== grep loss ===
[DataCollator] Original padding_side: left
[DataCollator] Set padding_side to: left
[DataCollator] Reason first mode: False
[DataCollator] Original padding_side: left
[DataCollator] Set padding_side to: left
[DataCollator] Reason first mode: False
[DataCollator] Original padding_side: left
[DataCollator] Set padding_side to: left
[DataCollator] Reason first mode: False
[DataCollator] Original padding_side: left
[DataCollator] Set padding_side to: left
[DataCollator] Reason first mode: False
[stdout]
`torch_dtype` is deprecated! Use `dtype` instead!
Using the latest cached version of the dataset since siyanzhao/Openthoughts_math_30k_opsd couldn't be found on the Hugging Face Hub (offline mode is enabled).
Found the latest cached dataset configuration 'default' at /opt/hf_cache/datasets/siyanzhao___openthoughts_math_30k_opsd/default/0.0.0/1f33e9dc2e8a1c639ca74f8024ad4a9f1f5eae62 (last modified on Sun Aug 2 17:14:36 2026).
Using the latest cached version of the dataset since siyanzhao/Openthoughts_math_30k_opsd couldn't be found on the Hugging Face Hub (offline mode is enabled).
Found the latest cached dataset configuration 'default' at /opt/hf_cache/datasets/siyanzhao___openthoughts_math_30k_opsd/default/0.0.0/1f33e9dc2e8a1c639ca74f8024ad4a9f1f5eae62 (last modified on Sun Aug 2 17:14:36 2026).
Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]Using the latest cached version of the dataset since siyanzhao/Openthoughts_math_30k_opsd couldn't be found on the Hugging Face Hub (offline mode is enabled).
Found the latest cached dataset configuration 'default' at /opt/hf_cache/datasets/siyanzhao___openthoughts_math_30k_opsd/default/0.0.0/1f33e9dc2e8a1c639ca74f8024ad4a9f1f5eae62 (last modified on Sun Aug 2 17:14:36 2026).
[DataCollator] Original padding_side: left
[DataCollator] Set padding_side to: left
[DataCollator] Reason first mode: False
[DataCollator] Original padding_side: left
[DataCollator] Set padding_side to: left
[DataCollator] Reason first mode: False
`torch_dtype` is deprecated! Use `dtype` instead!
`torch_dtype` is deprecated! Use `dtype` instead!
[DataCollator] Original padding_side: left
[DataCollator] Set padding_side to: left
[DataCollator] Reason first mode: False
Loading checkpoint shards: 100%|██████████| 2/2 [00:00<00:00, 36.18it/s]
`torch_dtype` is deprecated! Use `dtype` instead!
Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]
Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]
Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]
Loading checkpoint shards: 100%|██████████| 2/2 [00:00<00:00, 36.35it/s]
Loading checkpoint shards: 100%|██████████| 2/2 [00:00<00:00, 39.76it/s]
Loading checkpoint shards: 100%|██████████| 2/2 [00:00<00:00, 35.95it/s]
Converting train dataset to ChatML: 0%| | 0/29434 [00:00<?, ? examples/s]
Converting train dataset to ChatML: 3%|▎ | 776/29434 [00:00<00:03, 7584.11 examples/s]
Converting train dataset to ChatML: 6%|▌ | 1790/29434 [00:00<00:04, 5596.30 examples/s]
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Converting train dataset to ChatML: 13%|█▎ | 3810/29434 [00:00<00:04, 6089.20 examples/s]
Converting train dataset to ChatML: 16%|█▋ | 4833/29434 [00:00<00:04, 6099.49 examples/s]
Converting train dataset to ChatML: 20%|█▉ | 5806/29434 [00:00<00:03, 5973.05 examples/s]
Converting train dataset to ChatML: 23%|██▎ | 6831/29434 [00:01<00:03, 5892.29 examples/s]
Converting train dataset to ChatML: 26%|██▋ | 7761/29434 [00:01<00:03, 5494.50 examples/s]
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Converting train dataset to ChatML: 31%|███ | 9000/29434 [00:01<00:04, 5062.67 examples/s]
Converting train dataset to ChatML: 34%|███▎ | 9861/29434 [00:01<00:03, 5857.03 examples/s]
Converting train dataset to ChatML: 37%|███▋ | 10795/29434 [00:01<00:03, 5819.78 examples/s]
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Converting train dataset to ChatML: 47%|████▋ | 13776/29434 [00:02<00:02, 5500.44 examples/s]
Converting train dataset to ChatML: 49%|████▉ | 14385/29434 [00:02<00:02, 5337.24 examples/s]
Converting train dataset to ChatML: 51%|█████ | 15000/29434 [00:02<00:02, 5173.33 examples/s]
Converting train dataset to ChatML: 54%|█████▍ | 15825/29434 [00:02<00:02, 5905.46 examples/s]
Converting train dataset to ChatML: 57%|█████▋ | 16788/29434 [00:02<00:02, 5764.52 examples/s]
Converting train dataset to ChatML: 59%|█████▉ | 17395/29434 [00:03<00:02, 5544.36 examples/s]
Converting train dataset to ChatML: 61%|██████ | 18000/29434 [00:03<00:02, 5392.16 examples/s]
Converting train dataset to ChatML: 64%|██████▎ | 18758/29434 [00:03<00:01, 5930.66 examples/s]
Converting train dataset to ChatML: 66%|██████▌ | 19397/29434 [00:03<00:01, 5185.04 examples/s]
Converting train dataset to ChatML: 68%|██████▊ | 20000/29434 [00:03<00:01, 5042.59 examples/s]
Converting train dataset to ChatML: 71%|███████ | 20781/29434 [00:03<00:01, 5715.93 examples/s]
Converting train dataset to ChatML: 73%|███████▎ | 21400/29434 [00:03<00:01, 5461.18 examples/s]
Converting train dataset to ChatML: 75%|███████▍ | 22000/29434 [00:03<00:01, 5457.96 examples/s]
Converting train dataset to ChatML: 78%|███████▊ | 22847/29434 [00:04<00:01, 6248.25 examples/s]
Converting train dataset to ChatML: 81%|████████ | 23885/29434 [00:04<00:00, 6380.16 examples/s]
Converting train dataset to ChatML: 85%|████████▍ | 24907/29434 [00:04<00:00, 6522.92 examples/s]
Converting train dataset to ChatML: 88%|████████▊ | 25927/29434 [00:04<00:00, 6251.75 examples/s]
Converting train dataset to ChatML: 91%|█████████▏| 26905/29434 [00:04<00:00, 6332.05 examples/s]
Converting train dataset to ChatML: 94%|█████████▍| 27790/29434 [00:04<00:00, 6197.03 examples/s]
Converting train dataset to ChatML: 98%|█████████▊| 28744/29434 [00:05<00:00, 5940.16 examples/s]
Converting train dataset to ChatML: 100%|█████████▉| 29420/29434 [00:05<00:00, 5696.77 examples/s]
Converting train dataset to ChatML: 100%|██████████| 29434/29434 [00:05<00:00, 5689.47 examples/s]
Tokenizing train dataset: 0%| | 0/29434 [00:00<?, ? examples/s]
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Tokenizing train dataset: 2%|▏ | 706/29434 [00:05<03:22, 141.79 examples/s]
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Tokenizing train dataset: 14%|█▍ | 4256/29434 [00:32<02:59, 140.47 examples/s]=== grep loss ===
[DataCollator] Original padding_side: left
[DataCollator] Set padding_side to: left
[DataCollator] Reason first mode: False
[DataCollator] Original padding_side: left
[DataCollator] Set padding_side to: left
[DataCollator] Reason first mode: False
[DataCollator] Original padding_side: left
[DataCollator] Set padding_side to: left
[DataCollator] Reason first mode: False
[DataCollator] Original padding_side: left
[DataCollator] Set padding_side to: left
[DataCollator] Reason first mode: False
[metadata] {"noOutputExpected": false}#35
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashInspect dataset columns
args
{
"command": "python - <<'EOF' 2>/dev/null\nimport os\nos.environ[\"HF_HOME\"]=\"/opt/hf_cache\"; os.environ[\"HF_HUB_OFFLINE\"]=\"1\"\nfrom datasets import load_dataset\nimport collections, numpy as np\nds = load_dataset(\"siyanzhao/Openthoughts_math_30k_opsd\")[\"train\"]\nprint(collections.Counter(ds[\"source\"]).most_common(10))\nc = ds[\"correct\"]\nprint(\"correct:\", collections.Counter([str(x) for x in c]).most_common(6))\ng = np.array([x if x is not None else -1 for x in ds[\"generated_token_count\"]])\nprint(\"gen_token_count:\", g.min(), g.max(), np.percentile(g,[5,25,50,75,95]).round().tolist())\nr0 = ds[0]\nfor k in ds.column_names:\n v = r0[k]\n print(\"=====\", k, type(v), str(v)[:400].replace(\"\\n\",\" \")[:400])\nEOF",
"description": "Inspect dataset columns"
}Bash result
[('olympiads', 21315), ('math', 5351), ('aops_forum', 2291), ('amc_aime', 477)]
correct: [('True', 29434)]
gen_token_count: 398 4999 [1169.0, 1964.0, 2853.0, 3813.0, 4737.0]
===== source <class 'str'> olympiads
===== problem <class 'str'> Given real numbers \( a, b, c \) and a positive number \( \lambda \) such that the polynomial \( f(x) = x^3 + a x^2 + b x + c \) has three real roots \( x_1, x_2, x_3 \), and the conditions \( x_2 - x_1 = \lambda \) and \( x_3 > \frac{1}{2}(x_1 + x_2) \) are satisfied, find the maximum value of \( \frac{2 a^3 + 27 c - 9 a b}{\lambda^3} \).
===== solution <class 'str'> We begin by analyzing the function \( f(x) = x^3 + a x^2 + b x + c \), which has three real roots \( x_1, x_2, x_3 \). We are given the following conditions: 1. \( x_2 - x_1 = \lambda \) 2. \( x_3 > \frac{1}{2} (x_1 + x_2) \) We aim to find the maximum value of \( \frac{2a^3 + 27c - 9ab}{\lambda^3} \). 1. **Transform the polynomial to remove the quadratic term:** Substitute \( x = y - \frac{
===== messages <class 'list'> [{'content': 'Given real numbers \\( a, b, c \\) and a positive number \\( \\lambda \\) such that the polynomial \\( f(x) = x^3 + a x^2 + b x + c \\) has three real roots \\( x_1, x_2, x_3 \\), and the conditions \\( x_2 - x_1 = \\lambda \\) and \\( x_3 > \\frac{1}{2}(x_1 + x_2) \\) are satisfied, find the maximum value of \\( \\frac{2 a^3 + 27 c - 9 a b}{\\lambda^3} \\).', 'role': 'user'}, {'cont
===== system <class 'str'> Your role as an assistant involves thoroughly exploring questions through a systematic long thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. Please structure your response into two main se
===== conversations <class 'list'> [{'from': 'user', 'value': 'Return your final response within \\boxed{}. Given real numbers \\( a, b, c \\) and a positive number \\( \\lambda \\) such that the polynomial \\( f(x) = x^3 + a x^2 + b x + c \\) has three real roots \\( x_1, x_2, x_3 \\), and the conditions \\( x_2 - x_1 = \\lambda \\) and \\( x_3 > \\frac{1}{2}(x_1 + x_2) \\) are satisfied, find the maximum value of \\( \\frac{2 a^3
===== generated_token_count <class 'int'> 4978
===== correct <class 'bool'> True
===== Question <class 'str'> Return your final response within \boxed{}. Given real numbers \( a, b, c \) and a positive number \( \lambda \) such that the polynomial \( f(x) = x^3 + a x^2 + b x + c \) has three real roots \( x_1, x_2, x_3 \), and the conditions \( x_2 - x_1 = \lambda \) and \( x_3 > \frac{1}{2}(x_1 + x_2) \) are satisfied, find the maximum value of \( \frac{2 a^3 + 27 c - 9 a b}{\lambda^3} \).
===== COT_Reason <class 'str'> Okay, let's try to tackle this problem step by step. The problem is about finding the maximum value of the expression (2a³ + 27c - 9ab)/λ³ given some conditions on the roots of the cubic polynomial f(x) = x³ + ax² + bx + c. The roots are real, with x₂ - x₁ = λ and x₃ > (x₁ + x₂)/2. First, I need to recall that for a cubic polynomial with roots x₁, x₂, x₃, the coefficients can be expressed using
===== Answer <class 'str'> \frac{3\sqrt{3}}{2}
[stdout]
[('olympiads', 21315), ('math', 5351), ('aops_forum', 2291), ('amc_aime', 477)]
correct: [('True', 29434)]
gen_token_count: 398 4999 [1169.0, 1964.0, 2853.0, 3813.0, 4737.0]
===== source <class 'str'> olympiads
===== problem <class 'str'> Given real numbers \( a, b, c \) and a positive number \( \lambda \) such that the polynomial \( f(x) = x^3 + a x^2 + b x + c \) has three real roots \( x_1, x_2, x_3 \), and the conditions \( x_2 - x_1 = \lambda \) and \( x_3 > \frac{1}{2}(x_1 + x_2) \) are satisfied, find the maximum value of \( \frac{2 a^3 + 27 c - 9 a b}{\lambda^3} \).
===== solution <class 'str'> We begin by analyzing the function \( f(x) = x^3 + a x^2 + b x + c \), which has three real roots \( x_1, x_2, x_3 \). We are given the following conditions: 1. \( x_2 - x_1 = \lambda \) 2. \( x_3 > \frac{1}{2} (x_1 + x_2) \) We aim to find the maximum value of \( \frac{2a^3 + 27c - 9ab}{\lambda^3} \). 1. **Transform the polynomial to remove the quadratic term:** Substitute \( x = y - \frac{
===== messages <class 'list'> [{'content': 'Given real numbers \\( a, b, c \\) and a positive number \\( \\lambda \\) such that the polynomial \\( f(x) = x^3 + a x^2 + b x + c \\) has three real roots \\( x_1, x_2, x_3 \\), and the conditions \\( x_2 - x_1 = \\lambda \\) and \\( x_3 > \\frac{1}{2}(x_1 + x_2) \\) are satisfied, find the maximum value of \\( \\frac{2 a^3 + 27 c - 9 a b}{\\lambda^3} \\).', 'role': 'user'}, {'cont
===== system <class 'str'> Your role as an assistant involves thoroughly exploring questions through a systematic long thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. Please structure your response into two main se
===== conversations <class 'list'> [{'from': 'user', 'value': 'Return your final response within \\boxed{}. Given real numbers \\( a, b, c \\) and a positive number \\( \\lambda \\) such that the polynomial \\( f(x) = x^3 + a x^2 + b x + c \\) has three real roots \\( x_1, x_2, x_3 \\), and the conditions \\( x_2 - x_1 = \\lambda \\) and \\( x_3 > \\frac{1}{2}(x_1 + x_2) \\) are satisfied, find the maximum value of \\( \\frac{2 a^3
===== generated_token_count <class 'int'> 4978
===== correct <class 'bool'> True
===== Question <class 'str'> Return your final response within \boxed{}. Given real numbers \( a, b, c \) and a positive number \( \lambda \) such that the polynomial \( f(x) = x^3 + a x^2 + b x + c \) has three real roots \( x_1, x_2, x_3 \), and the conditions \( x_2 - x_1 = \lambda \) and \( x_3 > \frac{1}{2}(x_1 + x_2) \) are satisfied, find the maximum value of \( \frac{2 a^3 + 27 c - 9 a b}{\lambda^3} \).
===== COT_Reason <class 'str'> Okay, let's try to tackle this problem step by step. The problem is about finding the maximum value of the expression (2a³ + 27c - 9ab)/λ³ given some conditions on the roots of the cubic polynomial f(x) = x³ + ax² + bx + c. The roots are real, with x₂ - x₁ = λ and x₃ > (x₁ + x₂)/2. First, I need to recall that for a cubic polynomial with roots x₁, x₂, x₃, the coefficients can be expressed using
===== Answer <class 'str'> \frac{3\sqrt{3}}{2}
[metadata] {"noOutputExpected": false}#36
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashFiltered smoke output
args
{
"command": "python - <<'EOF'\nt=open('/workspace/smokeA.out',errors='ignore').read()\nimport re\nkeys=[\"'loss'\",\"Traceback\",\"Error:\",\"clip mode\",\"train_runtime\",\"vLLM generation done\"]\nlines=[l for l in t.split('\\n') if any(k in l for k in keys)]\nfor l in lines[-14:]: print(l.strip()[:250])\nprint(\"LEN\", len(t))\nEOF\nnvidia-smi --query-gpu=index,memory.used --format=csv,noheader",
"description": "Filtered smoke output"
}Bash result
LEN 71649
0, 1043 MiB
1, 1333 MiB
2, 1333 MiB
3, 1333 MiB
[stdout]
LEN 71649
0, 1043 MiB
1, 1333 MiB
2, 1333 MiB
3, 1333 MiB
[metadata] {"noOutputExpected": false}toolBashCheck eval prompt format
args
{
"command": "python - <<'EOF'\nt=open('/workspace/eval_official.py').read()\nimport re\ni=t.find('def build_prompt')\nprint(t[:1200] if i<0 else t[i-500:i+2500])\nEOF",
"description": "Check eval prompt format"
}Bash result
"""Official evaluation for the OPSD research-intuition task (4-GPU, data-parallel).
This is the single source of truth for evaluation. The solver-facing
/workspace/dev_eval.sh and the hidden verifier run THIS SAME logic with THE
SAME frozen settings (paper Table 8 / README "Evaluation settings"):
benchmark : AIME 2024 (HuggingFaceH4/aime_2024 @ 2fe88a2, 30 problems)
metric : Avg@12 accuracy (math_verify on \\boxed{} answers)
temperature : 1.0
top_p : 0.95 (Qwen3 thinking-mode default used by the repo eval)
top_k : -1 (disabled)
min_p : 0.0
presence_penalty : 0.0
max_new_tokens : 38912
thinking mode : enabled
samples/problem : 12
sampling seed : 20260610 (fixed for the official run)
Parallelism: because Qwen3-1.7B is small, tensor-parallel generation scales
poorly (per-layer all-reduce dominates) and does NOT add request-level
parallelism. Instead we run DATA-PARALLEL: `--data_parallel_size` independent
single-GPU vLLM engines (tensor_parallel_size=1 each), each pinned to one GPU
via CUDA_VISIBLE_DEVICES and handling a round-robin shard of the problems. This
scales close
[stdout]
"""Official evaluation for the OPSD research-intuition task (4-GPU, data-parallel).
This is the single source of truth for evaluation. The solver-facing
/workspace/dev_eval.sh and the hidden verifier run THIS SAME logic with THE
SAME frozen settings (paper Table 8 / README "Evaluation settings"):
benchmark : AIME 2024 (HuggingFaceH4/aime_2024 @ 2fe88a2, 30 problems)
metric : Avg@12 accuracy (math_verify on \\boxed{} answers)
temperature : 1.0
top_p : 0.95 (Qwen3 thinking-mode default used by the repo eval)
top_k : -1 (disabled)
min_p : 0.0
presence_penalty : 0.0
max_new_tokens : 38912
thinking mode : enabled
samples/problem : 12
sampling seed : 20260610 (fixed for the official run)
Parallelism: because Qwen3-1.7B is small, tensor-parallel generation scales
poorly (per-layer all-reduce dominates) and does NOT add request-level
parallelism. Instead we run DATA-PARALLEL: `--data_parallel_size` independent
single-GPU vLLM engines (tensor_parallel_size=1 each), each pinned to one GPU
via CUDA_VISIBLE_DEVICES and handling a round-robin shard of the problems. This
scales close
[metadata] {"noOutputExpected": false}#37
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashTail of smoke log
args
{
"command": "python - <<'EOF'\nt=open('/workspace/smokeA.out',errors='ignore').read()\n# strip carriage-return progress bars\nlines=[l.split('\\r')[-1] for l in t.split('\\n')]\nfor l in lines[-30:]: print(l[:220])\nEOF",
"description": "Tail of smoke log"
}Bash result
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[stdout]
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[metadata] {"noOutputExpected": false}#38
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashEval prompt construction
args
{
"command": "python - <<'EOF'\nt=open('/workspace/eval_official.py').read()\ni=t.find('apply_chat_template')\nprint(t[max(0,i-1800):i+600])\nEOF",
"description": "Eval prompt construction"
}Bash result
AutoModelForCausalLM
lora_rank = 64
try:
with open(Path(ckpt) / "adapter_config.json") as f:
lora_rank = int(json.load(f).get("r", 64))
except Exception:
pass
if lora_rank > 256:
raise ValueError(f"LoRA rank {lora_rank} exceeds the allowed maximum of 256")
print("Merging LoRA adapter into base weights for evaluation (CPU, once)...")
base = AutoModelForCausalLM.from_pretrained(
args.base_model, torch_dtype=torch.bfloat16, trust_remote_code=True
)
merged = PeftModel.from_pretrained(base, ckpt).merge_and_unload()
merged_dir = tempfile.mkdtemp(prefix="opsd_merged_")
merged.save_pretrained(merged_dir, safe_serialization=True)
# Tokenizer / chat template ALWAYS from the frozen base model.
AutoTokenizer.from_pretrained(args.base_model, trust_remote_code=True).save_pretrained(merged_dir)
del base, merged
model_path = merged_dir
elif kind == "full":
model_path = ckpt
else:
model_path = args.base_model
# Tokenizer / chat template ALWAYS from the frozen base model.
tokenizer = AutoTokenizer.from_pretrained(args.base_model, trust_remote_code=True)
sampling_kwargs = dict(
temperature=FROZEN["temperature"],
top_p=FROZEN["top_p"],
top_k=FROZEN["top_k"],
min_p=FROZEN["min_p"],
presence_penalty=FROZEN["presence_penalty"],
max_tokens=args.max_new_tokens,
n=args.val_n,
seed=args.seed,
)
prompts, gt_answers = [], []
for ex in problems:
user_message = (
f"{ex['problem']}\n\nPlease reason step by step, and put your final answer within \\boxed{{}}."
)
text = tokenizer.apply_chat_template(
[{"role": "user", "content": user_message}],
tokenize=False,
add_generation_prompt=True,
enable_thinking=FROZEN["enable_thinking"],
)
prompts.append(text)
gt_answers.append(str(ex["answer"]))
# Never spawn more engines than problems (avoids empty shards on tiny partials).
dp_size = max(1, min(args.data_parallel_size, len(prompts)))
print(f"Generating with data_parallel_size={dp_size} (TP=1 per engine) ...")
gen_texts = generate_all(model_path, prompts, sampling_kwargs, args.seed, dp_si
[stdout]
AutoModelForCausalLM
lora_rank = 64
try:
with open(Path(ckpt) / "adapter_config.json") as f:
lora_rank = int(json.load(f).get("r", 64))
except Exception:
pass
if lora_rank > 256:
raise ValueError(f"LoRA rank {lora_rank} exceeds the allowed maximum of 256")
print("Merging LoRA adapter into base weights for evaluation (CPU, once)...")
base = AutoModelForCausalLM.from_pretrained(
args.base_model, torch_dtype=torch.bfloat16, trust_remote_code=True
)
merged = PeftModel.from_pretrained(base, ckpt).merge_and_unload()
merged_dir = tempfile.mkdtemp(prefix="opsd_merged_")
merged.save_pretrained(merged_dir, safe_serialization=True)
# Tokenizer / chat template ALWAYS from the frozen base model.
AutoTokenizer.from_pretrained(args.base_model, trust_remote_code=True).save_pretrained(merged_dir)
del base, merged
model_path = merged_dir
elif kind == "full":
model_path = ckpt
else:
model_path = args.base_model
# Tokenizer / chat template ALWAYS from the frozen base model.
tokenizer = AutoTokenizer.from_pretrained(args.base_model, trust_remote_code=True)
sampling_kwargs = dict(
temperature=FROZEN["temperature"],
top_p=FROZEN["top_p"],
top_k=FROZEN["top_k"],
min_p=FROZEN["min_p"],
presence_penalty=FROZEN["presence_penalty"],
max_tokens=args.max_new_tokens,
n=args.val_n,
seed=args.seed,
)
prompts, gt_answers = [], []
for ex in problems:
user_message = (
f"{ex['problem']}\n\nPlease reason step by step, and put your final answer within \\boxed{{}}."
)
text = tokenizer.apply_chat_template(
[{"role": "user", "content": user_message}],
tokenize=False,
add_generation_prompt=True,
enable_thinking=FROZEN["enable_thinking"],
)
prompts.append(text)
gt_answers.append(str(ex["answer"]))
# Never spawn more engines than problems (avoids empty shards on tiny partials).
dp_size = max(1, min(args.data_parallel_size, len(prompts)))
print(f"Generating with data_parallel_size={dp_size} (TP=1 per engine) ...")
gen_texts = generate_all(model_path, prompts, sampling_kwargs, args.seed, dp_si
[metadata] {"noOutputExpected": false}#39
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashCheck smoke status
args
{
"command": "python - <<'EOF'\nt=open('/workspace/smokeA.out',errors='ignore').read()\nlines=[l.split('\\r')[-1] for l in t.split('\\n')]\nsel=[l for l in lines if (\"'loss'\" in l or 'Traceback' in l or 'rror' in l or 'clip mode' in l or 'train_runtime' in l)]\nfor l in sel[-12:]: print(l[:250])\nprint(\"...tail:\", lines[-3][:150])\nEOF",
"description": "Check smoke status"
}Bash result
...tail: Tokenizing train dataset: 61%|██████ | 17867/29434 [02:19<01:29, 128.95 examples/s]
[stdout]
...tail: Tokenizing train dataset: 61%|██████ | 17867/29434 [02:19<01:29, 128.95 examples/s]
[metadata] {"noOutputExpected": false}#40
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolEdit
args
{
"replace_all": false,
"file_path": "/workspace/repo/data_collator.py",
"old_string": "import torch\n\n\nclass SelfDistillationDataCollator:",
"new_string": "import os\n\nimport torch\n\n# ---------------------------------------------------------------------------\n# Method switches (env-overridable so dev runs can A/B them; the defaults below\n# are what the training entrypoint uses, since the launcher passes no env vars).\n#\n# OPSD_STUDENT_PROMPT\n# \"eval\" : the student's rollout prompt is byte-identical to the deployment\n# prompt used at evaluation time. OPSD is an *on-policy* method, so\n# the state distribution the student is trained on should be the one\n# it is graded on; the released recipe prefixes \"Problem: \", which\n# the benchmark prompt does not, shifting every rollout off-policy\n# relative to deployment.\n# \"paper\" : released recipe (\"Problem: \" prefix).\n#\n# OPSD_TEACHER_CONTEXT\n# \"solution\" : released recipe -- privileged context is the concise reference\n# solution.\n# \"cot\" : privileged context is the dataset's verified long-form\n# reasoning trace (COT_Reason) instead. The concise solution is\n# terse and answer-oriented; the paper itself attributes SFT's\n# degradation on this data to that style (shorter test-time\n# reasoning). Conditioning the teacher on the exploratory trace\n# instead keeps the privileged information (it reaches the same\n# verified answer) while putting the teacher in the reflective\n# mode whose token distribution we actually want to distill.\n# \"both\" : concise solution followed by the long-form trace.\n# ---------------------------------------------------------------------------\nSTUDENT_PROMPT_STYLE = os.environ.get(\"OPSD_STUDENT_PROMPT\", \"eval\")\nTEACHER_CONTEXT = os.environ.get(\"OPSD_TEACHER_CONTEXT\", \"solution\")\n\n\nclass SelfDistillationDataCollator:"
}Edit result
The file /workspace/repo/data_collator.py has been updated successfully. (file state is current in your context — no need to Read it back)
[metadata] {"filePath": "/workspace/repo/data_collator.py", "oldString": "import torch\n\n\nclass SelfDistillationDataCollator:", "newString": "import os\n\nimport torch\n\n# ---------------------------------------------------------------------------\n# Method switches (env-overridable so dev runs can A/B them; the defaults below\n# are what the training entrypoint uses, since the launcher passes no env vars).\n#\n# OPSD_STUDENT_PROMPT\n# \"eval\" : the student's rollout prompt is byte-identical to the deployment\n# prompt used at evaluation time. OPSD is an *on-policy* method, so\n# the state distribution the student is trained on should be the one\n# it is graded on; the released recipe prefixes \"Problem: \", which\n# the benchmark prompt does not, shifting every rollout off-policy\n# relative to deployment.\n# \"paper\" : released recipe (\"Problem: \" prefix).\n#\n# OPSD_TEACHER_CONTEXT\n# \"solution\" : released recipe -- privileged context is the concise reference\n# solution.\n# \"cot\" : privileged context is the dataset's verified long-form\n# reasoning trace (COT_Reason) instead. The concise solution is\n# terse and answer-oriented; the paper itself attributes SFT's\n# degradation on this data to that style (shorter test-time\n# reasoning). Conditioning the teacher on the exploratory trace\n# instead keeps the privileged information (it reaches the same\n# verified answer) while putting the teacher in the reflective\n# mode whose token distribution we actually want to distill.\n# \"both\" : concise solution followed by the long-form trace.\n# ---------------------------------------------------------------------------\nSTUDENT_PROMPT_STYLE = os.environ.get(\"OPSD_STUDENT_PROMPT\", \"eval\")\nTEACHER_CONTEXT = os.environ.get(\"OPSD_TEACHER_CONTEXT\", \"solution\")\n\n\nclass SelfDistillationDataCollator:", "originalFile": null, "structuredPatch": [{"oldStart": 1, "oldLines": 6, "newStart": 1, "newLines": 37, "lines": ["+import os", "+", " import torch", " ", "+# ---------------------------------------------------------------------------", "+# Method switches (env-overridable so dev runs can A/B them; the defaults below", "+# are what the training entrypoint uses, since the launcher passes no env vars).", "+#", "+# OPSD_STUDENT_PROMPT", "+# \"eval\" : the student's rollout prompt is byte-identical to the deployment", "+# prompt used at evaluation time. OPSD is an *on-policy* method, so", "+# the state distribution the student is trained on should be the one", "+# it is graded on; the released recipe prefixes \"Problem: \", which", "+# the benchmark prompt does not, shifting every rollout off-policy", "+# relative to deployment.", "+# \"paper\" : released recipe (\"Problem: \" prefix).", "+#", "+# OPSD_TEACHER_CONTEXT", "+# \"solution\" : released recipe -- privileged context is the concise reference", "+# solution.", "+# \"cot\" : privileged context is the dataset's verified long-form", "+# reasoning trace (COT_Reason) instead. The concise solution is", "+# terse and answer-oriented; the paper itself attributes SFT's", "+# degradation on this data to that style (shorter test-time", "+# reasoning). Conditioning the teacher on the exploratory trace", "+# instead keeps the privileged information (it reaches the same", "+# verified answer) while putting the teacher in the reflective", "+# mode whose token distribution we actually want to distill.", "+# \"both\" : concise solution followed by the long-form trace.", "+# ---------------------------------------------------------------------------", "+STUDENT_PROMPT_STYLE = os.environ.get(\"OPSD_STUDENT_PROMPT\", \"eval\")", "+TEACHER_CONTEXT = os.environ.get(\"OPSD_TEACHER_CONTEXT\", \"solution\")", " ", "+", " class SelfDistillationDataCollator:", " \"\"\"", " Data collator for self-distillation that creates both student and teacher inputs."]}], "userModified": false, "replaceAll": false}#41
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolEdit
args
{
"replace_all": false,
"file_path": "/workspace/repo/data_collator.py",
"old_string": " problem = feature[\"problem\"]\n solution = feature[\"solution\"]\n\n # Student prompt: just the problem with instruction (matching evaluation format)\n student_user_message = f\"Problem: {problem}\\n\\nPlease reason step by step, and put your final answer within \\\\boxed{{}}.\"",
"new_string": " problem = feature[\"problem\"]\n solution = feature[\"solution\"]\n if TEACHER_CONTEXT == \"cot\":\n solution = feature.get(\"COT_Reason\") or solution\n elif TEACHER_CONTEXT == \"both\":\n cot = feature.get(\"COT_Reason\")\n if cot:\n solution = f\"{solution}\\n\\nA detailed reasoning trace reaching the same answer:\\n{cot}\"\n\n # Student prompt: just the problem with instruction (matching evaluation format)\n if STUDENT_PROMPT_STYLE == \"eval\":\n student_user_message = f\"{problem}\\n\\nPlease reason step by step, and put your final answer within \\\\boxed{{}}.\"\n else:\n student_user_message = f\"Problem: {problem}\\n\\nPlease reason step by step, and put your final answer within \\\\boxed{{}}.\""
}Edit result
The file /workspace/repo/data_collator.py has been updated successfully. (file state is current in your context — no need to Read it back)
[metadata] {"filePath": "/workspace/repo/data_collator.py", "oldString": " problem = feature[\"problem\"]\n solution = feature[\"solution\"]\n\n # Student prompt: just the problem with instruction (matching evaluation format)\n student_user_message = f\"Problem: {problem}\\n\\nPlease reason step by step, and put your final answer within \\\\boxed{{}}.\"", "newString": " problem = feature[\"problem\"]\n solution = feature[\"solution\"]\n if TEACHER_CONTEXT == \"cot\":\n solution = feature.get(\"COT_Reason\") or solution\n elif TEACHER_CONTEXT == \"both\":\n cot = feature.get(\"COT_Reason\")\n if cot:\n solution = f\"{solution}\\n\\nA detailed reasoning trace reaching the same answer:\\n{cot}\"\n\n # Student prompt: just the problem with instruction (matching evaluation format)\n if STUDENT_PROMPT_STYLE == \"eval\":\n student_user_message = f\"{problem}\\n\\nPlease reason step by step, and put your final answer within \\\\boxed{{}}.\"\n else:\n student_user_message = f\"Problem: {problem}\\n\\nPlease reason step by step, and put your final answer within \\\\boxed{{}}.\"", "originalFile": null, "structuredPatch": [{"oldStart": 104, "oldLines": 9, "newStart": 104, "newLines": 18, "lines": [" # Handle different possible column names", " problem = feature[\"problem\"]", " solution = feature[\"solution\"]", "+ if TEACHER_CONTEXT == \"cot\":", "+ solution = feature.get(\"COT_Reason\") or solution", "+ elif TEACHER_CONTEXT == \"both\":", "+ cot = feature.get(\"COT_Reason\")", "+ if cot:", "+ solution = f\"{solution}\\n\\nA detailed reasoning trace reaching the same answer:\\n{cot}\"", " ", " # Student prompt: just the problem with instruction (matching evaluation format)", "- student_user_message = f\"Problem: {problem}\\n\\nPlease reason step by step, and put your final answer within \\\\boxed{{}}.\"", "+ if STUDENT_PROMPT_STYLE == \"eval\":", "+ student_user_message = f\"{problem}\\n\\nPlease reason step by step, and put your final answer within \\\\boxed{{}}.\"", "+ else:", "+ student_user_message = f\"Problem: {problem}\\n\\nPlease reason step by step, and put your final answer within \\\\boxed{{}}.\"", " student_messages = [{\"role\": \"user\", \"content\": student_user_message}]", " ", " # Apply chat template for student (matching evaluation)"]}], "userModified": false, "replaceAll": false}#42
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashCheck signature columns
args
{
"command": "sed -n 395,420p /workspace/repo/opsd_trainer.py",
"description": "Check signature columns"
}Bash result
reduction="batchmean",
logits_are_probs=False,
top_k=None,
token_clip=None,
clip_mode="entry",
):
"""
Compute the generalized Jensen-Shannon Divergence loss for knowledge distillation using F.kl_div. See Eq. (1)
of https://huggingface.co/papers/2306.13649 for the definition.
Args:
student_logits:
Tensor of shape (batch_size, sequence_length, vocab_size)
teacher_logits:
Tensor of shape (batch_size, sequence_length, vocab_size)
labels:
Tensor of shape (batch_size, sequence_length) with -100 for padding tokens to ignore when computing
loss
beta:
Interpolation coefficient between 0 and 1 (default: 0.5)
temperature:
Softmax temperature (default: 1.0)
reduction:
Specifies the reduction to apply to the output (default: 'batchmean')
top_k:
If set, restricts the loss to only the top-k tokens of the teacher distribution. Both student and
[stdout]
reduction="batchmean",
logits_are_probs=False,
top_k=None,
token_clip=None,
clip_mode="entry",
):
"""
Compute the generalized Jensen-Shannon Divergence loss for knowledge distillation using F.kl_div. See Eq. (1)
of https://huggingface.co/papers/2306.13649 for the definition.
Args:
student_logits:
Tensor of shape (batch_size, sequence_length, vocab_size)
teacher_logits:
Tensor of shape (batch_size, sequence_length, vocab_size)
labels:
Tensor of shape (batch_size, sequence_length) with -100 for padding tokens to ignore when computing
loss
beta:
Interpolation coefficient between 0 and 1 (default: 0.5)
temperature:
Softmax temperature (default: 1.0)
reduction:
Specifies the reduction to apply to the output (default: 'batchmean')
top_k:
If set, restricts the loss to only the top-k tokens of the teacher distribution. Both student and
[metadata] {"noOutputExpected": false}#43
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashRead signature columns method
args
{
"command": "python - <<'EOF'\nt=open('/workspace/repo/opsd_trainer.py').read()\ni=t.find('_set_signature_columns_if_needed')\nprint(t[i-50:i+900])\nEOF",
"description": "Read signature columns method"
}Bash result
add_callback(GOLDVLLMSyncCallback(self))
def _set_signature_columns_if_needed(self):
super()._set_signature_columns_if_needed()
required_columns = [
"problem",
"solution",
]
if self._signature_columns is None:
self._signature_columns = required_columns
else:
for column in required_columns:
if column not in self._signature_columns:
self._signature_columns.append(column)
@staticmethod
def generalized_jsd_loss(
student_logits,
teacher_logits,
labels=None,
beta=0.5,
temperature=1.0,
reduction="batchmean",
logits_are_probs=False,
top_k=None,
token_clip=None,
clip_mode="entry",
):
"""
Compute the generalized Jensen-Shannon Divergence loss for knowledge distillation using F.kl_div. See Eq. (1)
of https
[stdout]
add_callback(GOLDVLLMSyncCallback(self))
def _set_signature_columns_if_needed(self):
super()._set_signature_columns_if_needed()
required_columns = [
"problem",
"solution",
]
if self._signature_columns is None:
self._signature_columns = required_columns
else:
for column in required_columns:
if column not in self._signature_columns:
self._signature_columns.append(column)
@staticmethod
def generalized_jsd_loss(
student_logits,
teacher_logits,
labels=None,
beta=0.5,
temperature=1.0,
reduction="batchmean",
logits_are_probs=False,
top_k=None,
token_clip=None,
clip_mode="entry",
):
"""
Compute the generalized Jensen-Shannon Divergence loss for knowledge distillation using F.kl_div. See Eq. (1)
of https
[metadata] {"noOutputExpected": false}#44
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolEdit
args
{
"replace_all": false,
"file_path": "/workspace/repo/opsd_trainer.py",
"old_string": " required_columns = [\n \"problem\",\n \"solution\",\n ]",
"new_string": " required_columns = [\n \"problem\",\n \"solution\",\n # long-form verified reasoning trace; used when the teacher's privileged\n # context is the trace rather than the concise solution (see data_collator)\n \"COT_Reason\",\n ]"
}Edit result
The file /workspace/repo/opsd_trainer.py has been updated successfully. (file state is current in your context — no need to Read it back)
[metadata] {"filePath": "/workspace/repo/opsd_trainer.py", "oldString": " required_columns = [\n \"problem\",\n \"solution\",\n ]", "newString": " required_columns = [\n \"problem\",\n \"solution\",\n # long-form verified reasoning trace; used when the teacher's privileged\n # context is the trace rather than the concise solution (see data_collator)\n \"COT_Reason\",\n ]", "originalFile": null, "structuredPatch": [{"oldStart": 377, "oldLines": 6, "newStart": 377, "newLines": 9, "lines": [" required_columns = [", " \"problem\",", " \"solution\",", "+ # long-form verified reasoning trace; used when the teacher's privileged", "+ # context is the trace rather than the concise solution (see data_collator)", "+ \"COT_Reason\",", " ]", " if self._signature_columns is None:", " self._signature_columns = required_columns"]}], "userModified": false, "replaceAll": false}#45
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashCheck smoke status again
args
{
"command": "python - <<'EOF'\nt=open('/workspace/smokeA.out',errors='ignore').read()\nlines=[l.split('\\r')[-1] for l in t.split('\\n')]\nsel=[l for l in lines if (\"'loss'\" in l or 'Traceback' in l or 'rror' in l or 'clip mode' in l or 'train_runtime' in l or 'STUDENT GENERATION' in l)]\nfor l in sel[-14:]: print(l[:250])\nprint(\"...tail:\", lines[-2][:160])\nEOF",
"description": "Check smoke status again"
}Bash result
...tail: Tokenizing train dataset: 83%|████████▎ | 24457/29434 [03:07<00:30, 164.61 examples/s]
[stdout]
...tail: Tokenizing train dataset: 83%|████████▎ | 24457/29434 [03:07<00:30, 164.61 examples/s]
[metadata] {"noOutputExpected": false}#46
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolEdit
args
{
"replace_all": false,
"file_path": "/workspace/repo/opsd_trainer.py",
"old_string": " inputs[\"labels\"] = labels\n\n # Log prompt and completion texts",
"new_string": " inputs[\"labels\"] = labels\n\n # One-time alignment self-check: with left-padded prompts the supervised span must be\n # exactly the sampled completion, and the prompt block must end flush against it (no\n # pad tokens wedged in between, which would shift every completion token's RoPE\n # position away from where it was sampled).\n if not getattr(self, \"_alignment_checked\", False) and self.accelerator.is_main_process:\n self._alignment_checked = True\n pad_id = self.processing_class.pad_token_id\n s_ids, t_ids = inputs[\"student_input_ids\"], inputs[\"teacher_input_ids\"]\n t_len = inputs[\"teacher_prompt_length\"]\n print(f\"\\n[OPSD align] student_prompt_block={student_prompt_len} teacher_prompt_block={t_len}\")\n print(f\"[OPSD align] student last prompt tokens (row0): \"\n f\"{self.processing_class.decode(s_ids[0, student_prompt_len - 6:student_prompt_len])!r}\")\n print(f\"[OPSD align] teacher last prompt tokens (row0): \"\n f\"{self.processing_class.decode(t_ids[0, t_len - 6:t_len])!r}\")\n print(f\"[OPSD align] supervised span (row0) starts: \"\n f\"{self.processing_class.decode(s_ids[0][labels[0] != -100][:12])!r}\")\n gap_s = int((s_ids[:, :student_prompt_len] == pad_id).sum(1).max())\n gap_t = int((t_ids[:, :t_len] == pad_id).sum(1).max())\n trail_s = int((s_ids[:, student_prompt_len - 1] == pad_id).sum())\n trail_t = int((t_ids[:, t_len - 1] == pad_id).sum())\n print(f\"[OPSD align] pads inside prompt blocks: student={gap_s} teacher={gap_t} \"\n f\"(these are LEADING pads and are harmless)\")\n print(f\"[OPSD align] rows whose prompt block ENDS in a pad (must be 0): \"\n f\"student={trail_s} teacher={trail_t}\\n\")\n\n # Log prompt and completion texts"
}Edit result
The file /workspace/repo/opsd_trainer.py has been updated successfully. (file state is current in your context — no need to Read it back)
[metadata] {"filePath": "/workspace/repo/opsd_trainer.py", "oldString": " inputs[\"labels\"] = labels\n\n # Log prompt and completion texts", "newString": " inputs[\"labels\"] = labels\n\n # One-time alignment self-check: with left-padded prompts the supervised span must be\n # exactly the sampled completion, and the prompt block must end flush against it (no\n # pad tokens wedged in between, which would shift every completion token's RoPE\n # position away from where it was sampled).\n if not getattr(self, \"_alignment_checked\", False) and self.accelerator.is_main_process:\n self._alignment_checked = True\n pad_id = self.processing_class.pad_token_id\n s_ids, t_ids = inputs[\"student_input_ids\"], inputs[\"teacher_input_ids\"]\n t_len = inputs[\"teacher_prompt_length\"]\n print(f\"\\n[OPSD align] student_prompt_block={student_prompt_len} teacher_prompt_block={t_len}\")\n print(f\"[OPSD align] student last prompt tokens (row0): \"\n f\"{self.processing_class.decode(s_ids[0, student_prompt_len - 6:student_prompt_len])!r}\")\n print(f\"[OPSD align] teacher last prompt tokens (row0): \"\n f\"{self.processing_class.decode(t_ids[0, t_len - 6:t_len])!r}\")\n print(f\"[OPSD align] supervised span (row0) starts: \"\n f\"{self.processing_class.decode(s_ids[0][labels[0] != -100][:12])!r}\")\n gap_s = int((s_ids[:, :student_prompt_len] == pad_id).sum(1).max())\n gap_t = int((t_ids[:, :t_len] == pad_id).sum(1).max())\n trail_s = int((s_ids[:, student_prompt_len - 1] == pad_id).sum())\n trail_t = int((t_ids[:, t_len - 1] == pad_id).sum())\n print(f\"[OPSD align] pads inside prompt blocks: student={gap_s} teacher={gap_t} \"\n f\"(these are LEADING pads and are harmless)\")\n print(f\"[OPSD align] rows whose prompt block ENDS in a pad (must be 0): \"\n f\"student={trail_s} teacher={trail_t}\\n\")\n\n # Log prompt and completion texts", "originalFile": null, "structuredPatch": [{"oldStart": 1460, "oldLines": 6, "newStart": 1460, "newLines": 31, "lines": [" ", " inputs[\"labels\"] = labels", " ", "+ # One-time alignment self-check: with left-padded prompts the supervised span must be", "+ # exactly the sampled completion, and the prompt block must end flush against it (no", "+ # pad tokens wedged in between, which would shift every completion token's RoPE", "+ # position away from where it was sampled).", "+ if not getattr(self, \"_alignment_checked\", False) and self.accelerator.is_main_process:", "+ self._alignment_checked = True", "+ pad_id = self.processing_class.pad_token_id", "+ s_ids, t_ids = inputs[\"student_input_ids\"], inputs[\"teacher_input_ids\"]", "+ t_len = inputs[\"teacher_prompt_length\"]", "+ print(f\"\\n[OPSD align] student_prompt_block={student_prompt_len} teacher_prompt_block={t_len}\")", "+ print(f\"[OPSD align] student last prompt tokens (row0): \"", "+ f\"{self.processing_class.decode(s_ids[0, student_prompt_len - 6:student_prompt_len])!r}\")", "+ print(f\"[OPSD align] teacher last prompt tokens (row0): \"", "+ f\"{self.processing_class.decode(t_ids[0, t_len - 6:t_len])!r}\")", "+ print(f\"[OPSD align] supervised span (row0) starts: \"", "+ f\"{self.processing_class.decode(s_ids[0][labels[0] != -100][:12])!r}\")", "+ gap_s = int((s_ids[:, :student_prompt_len] == pad_id).sum(1).max())", "+ gap_t = int((t_ids[:, :t_len] == pad_id).sum(1).max())", "+ trail_s = int((s_ids[:, student_prompt_len - 1] == pad_id).sum())", "+ trail_t = int((t_ids[:, t_len - 1] == pad_id).sum())", "+ print(f\"[OPSD align] pads inside prompt blocks: student={gap_s} teacher={gap_t} \"", "+ f\"(these are LEADING pads and are harmless)\")", "+ print(f\"[OPSD align] rows whose prompt block ENDS in a pad (must be 0): \"", "+ f\"student={trail_s} teacher={trail_t}\\n\")", "+", " # Log prompt and completion texts", " self._textual_logs[\"prompt\"].extend(gather_object(prompt_texts))", " self._textual_logs[\"completion\"].extend(gather_object(completion_texts))"]}], "userModified": false, "replaceAll": false}#47
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolEdit
args
{
"replace_all": false,
"file_path": "/workspace/repo/opsd_trainer.py",
"old_string": " del student_logits_for_loss, teacher_logits_for_loss",
"new_string": " # Cheap running diagnostic: the true (unclipped) per-token forward KL between the\n # privileged teacher and the student along the rollout, i.e. the raw signal size.\n with torch.no_grad():\n m = shifted_labels != -100\n if m.any():\n tlp = F.log_softmax(teacher_logits_for_loss.float() / self.temperature, dim=-1)\n slp = F.log_softmax(student_logits_for_loss.float() / self.temperature, dim=-1)\n k = (tlp.exp() * (tlp - slp)).sum(-1)[m]\n self._kl_stat_sum += float(k.sum())\n self._kl_stat_n += int(m.sum())\n self._kl_stat_hi += int((k > (self.jsd_token_clip or 0.05)).sum())\n del tlp, slp, k\n if self._kl_stat_n >= 20000 and self.accelerator.is_main_process:\n print(f\"[OPSD kl] step {self.state.global_step}: mean per-token KL(T||S)=\"\n f\"{self._kl_stat_sum / self._kl_stat_n:.4f} over {self._kl_stat_n} tokens, \"\n f\"frac above tau={self._kl_stat_hi / self._kl_stat_n:.3f}\")\n self._kl_stat_sum, self._kl_stat_n, self._kl_stat_hi = 0.0, 0, 0\n\n del student_logits_for_loss, teacher_logits_for_loss"
}Edit result
The file /workspace/repo/opsd_trainer.py has been updated successfully. (file state is current in your context — no need to Read it back)
[metadata] {"filePath": "/workspace/repo/opsd_trainer.py", "oldString": " del student_logits_for_loss, teacher_logits_for_loss", "newString": " # Cheap running diagnostic: the true (unclipped) per-token forward KL between the\n # privileged teacher and the student along the rollout, i.e. the raw signal size.\n with torch.no_grad():\n m = shifted_labels != -100\n if m.any():\n tlp = F.log_softmax(teacher_logits_for_loss.float() / self.temperature, dim=-1)\n slp = F.log_softmax(student_logits_for_loss.float() / self.temperature, dim=-1)\n k = (tlp.exp() * (tlp - slp)).sum(-1)[m]\n self._kl_stat_sum += float(k.sum())\n self._kl_stat_n += int(m.sum())\n self._kl_stat_hi += int((k > (self.jsd_token_clip or 0.05)).sum())\n del tlp, slp, k\n if self._kl_stat_n >= 20000 and self.accelerator.is_main_process:\n print(f\"[OPSD kl] step {self.state.global_step}: mean per-token KL(T||S)=\"\n f\"{self._kl_stat_sum / self._kl_stat_n:.4f} over {self._kl_stat_n} tokens, \"\n f\"frac above tau={self._kl_stat_hi / self._kl_stat_n:.3f}\")\n self._kl_stat_sum, self._kl_stat_n, self._kl_stat_hi = 0.0, 0, 0\n\n del student_logits_for_loss, teacher_logits_for_loss", "originalFile": null, "structuredPatch": [{"oldStart": 777, "oldLines": 6, "newStart": 777, "newLines": 24, "lines": [" token_clip=self.jsd_token_clip,", " clip_mode=self.jsd_clip_mode,", " )", "+ # Cheap running diagnostic: the true (unclipped) per-token forward KL between the", "+ # privileged teacher and the student along the rollout, i.e. the raw signal size.", "+ with torch.no_grad():", "+ m = shifted_labels != -100", "+ if m.any():", "+ tlp = F.log_softmax(teacher_logits_for_loss.float() / self.temperature, dim=-1)", "+ slp = F.log_softmax(student_logits_for_loss.float() / self.temperature, dim=-1)", "+ k = (tlp.exp() * (tlp - slp)).sum(-1)[m]", "+ self._kl_stat_sum += float(k.sum())", "+ self._kl_stat_n += int(m.sum())", "+ self._kl_stat_hi += int((k > (self.jsd_token_clip or 0.05)).sum())", "+ del tlp, slp, k", "+ if self._kl_stat_n >= 20000 and self.accelerator.is_main_process:", "+ print(f\"[OPSD kl] step {self.state.global_step}: mean per-token KL(T||S)=\"", "+ f\"{self._kl_stat_sum / self._kl_stat_n:.4f} over {self._kl_stat_n} tokens, \"", "+ f\"frac above tau={self._kl_stat_hi / self._kl_stat_n:.3f}\")", "+ self._kl_stat_sum, self._kl_stat_n, self._kl_stat_hi = 0.0, 0, 0", "+", " del student_logits_for_loss, teacher_logits_for_loss", " ", " empty_cache()"]}], "userModified": false, "replaceAll": false}#48
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolEdit
args
{
"replace_all": false,
"file_path": "/workspace/repo/opsd_trainer.py",
"old_string": " print(f\"[OPSD] jsd clip mode: {self.jsd_clip_mode} (tau={self.jsd_token_clip})\")",
"new_string": " print(f\"[OPSD] jsd clip mode: {self.jsd_clip_mode} (tau={self.jsd_token_clip})\")\n self._kl_stat_sum, self._kl_stat_n, self._kl_stat_hi = 0.0, 0, 0"
}Edit result
The file /workspace/repo/opsd_trainer.py has been updated successfully. (file state is current in your context — no need to Read it back)
[metadata] {"filePath": "/workspace/repo/opsd_trainer.py", "oldString": " print(f\"[OPSD] jsd clip mode: {self.jsd_clip_mode} (tau={self.jsd_token_clip})\")", "newString": " print(f\"[OPSD] jsd clip mode: {self.jsd_clip_mode} (tau={self.jsd_token_clip})\")\n self._kl_stat_sum, self._kl_stat_n, self._kl_stat_hi = 0.0, 0, 0", "originalFile": null, "structuredPatch": [{"oldStart": 195, "oldLines": 6, "newStart": 195, "newLines": 7, "lines": [" if self.jsd_clip_mode not in (\"entry\", \"token\"):", " raise ValueError(f\"OPSD_CLIP_MODE must be 'entry' or 'token', got {self.jsd_clip_mode}\")", " print(f\"[OPSD] jsd clip mode: {self.jsd_clip_mode} (tau={self.jsd_token_clip})\")", "+ self._kl_stat_sum, self._kl_stat_n, self._kl_stat_hi = 0.0, 0, 0", " self.use_ema_teacher = use_ema_teacher", " self.ema_decay = ema_decay", " self._ema_params = None # lazily initialized on first optimizer step"]}], "userModified": false, "replaceAll": false}#49
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashCheck smoke status
args
{
"command": "python - <<'EOF'\nt=open('/workspace/smokeA.out',errors='ignore').read()\nlines=[l.split('\\r')[-1] for l in t.split('\\n')]\nsel=[l for l in lines if (\"'loss'\" in l or 'Traceback' in l or 'rror' in l or 'clip mode' in l or 'train_runtime' in l or 'align' in l)]\nfor l in sel[-16:]: print(l[:250])\nprint(\"...tail:\", lines[-2][:160])\nEOF",
"description": "Check smoke status"
}Bash result
...tail: Tokenizing train dataset: 99%|█████████▉| 29107/29434 [03:42<00:02, 113.46 examples/s]
[stdout]
...tail: Tokenizing train dataset: 99%|█████████▉| 29107/29434 [03:42<00:02, 113.46 examples/s]
[metadata] {"noOutputExpected": false}#50
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashWait and check smoke
args
{
"command": "sleep 150; python - <<'EOF'\nt=open('/workspace/smokeA.out',errors='ignore').read()\nlines=[l.split('\\r')[-1] for l in t.split('\\n')]\nsel=[l for l in lines if (\"'loss'\" in l or 'Traceback' in l or 'rror' in l or 'clip mode' in l or 'train_runtime' in l or 'align' in l or 'vLLM generation done' in l)]\nfor l in sel[-16:]: print(l[:250])\nprint(\"...tail:\", lines[-2][:160])\nEOF",
"description": "Wait and check smoke"
}Bash result · error
Exit code 143
Command timed out after 2m 0s
[error] tool reported failure