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: 9h 4m
recorded: 2026-08-03

run idopsd-aime24-4gpus__LSVoBuR

Everything claude-opus-5 did on this task, in order.

download JSON ↗

claude-codeclaude-opus-5145 turns21 narration148 action148 observation124 reasoning1 final

#26

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolWrite

args

{
  "file_path": "/workspace/submission/recipe.env",
  "content": "# OPSD method recipe — whitelisted hyper-parameter overrides (KEY=VALUE per line).\n#\n# The method itself lives in /workspace/repo (direction-preserving token-influence\n# clipping + padding-hole-free position ids). The only knob set here is the LR\n# schedule: the released OPSD recipe (train_baseline.sh / the reference run's logs)\n# leaves lr_scheduler_type at the HF default, i.e. linear decay to 0 over the 100\n# steps, which train_capped.sh would otherwise override to `constant`.\nlr_scheduler_type=linear\n"
}

Write result

The file /workspace/submission/recipe.env has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"type": "update", "filePath": "/workspace/submission/recipe.env", "content": "# OPSD method recipe — whitelisted hyper-parameter overrides (KEY=VALUE per line).\n#\n# The method itself lives in /workspace/repo (direction-preserving token-influence\n# clipping + padding-hole-free position ids). The only knob set here is the LR\n# schedule: the released OPSD recipe (train_baseline.sh / the reference run's logs)\n# leaves lr_scheduler_type at the HF default, i.e. linear decay to 0 over the 100\n# steps, which train_capped.sh would otherwise override to `constant`.\nlr_scheduler_type=linear\n", "structuredPatch": [{"oldStart": 1, "oldLines": 18, "newStart": 1, "newLines": 8, "lines": [" # OPSD method recipe — whitelisted hyper-parameter overrides (KEY=VALUE per line).", "-# Empty/all-commented == the OPSD baseline recipe on top of your /workspace/repo", "-# code changes. The scorer re-trains with THIS file + your repo for 100 steps.", "-# Budget knobs (max_steps, epochs, batch, gradient_accumulation, num_processes,", "-# model) are FIXED by the scorer and ignored here.", " #", "-# learning_rate=5e-6", "-# lora_r=64", "-# lora_alpha=128", "-# beta=0", "-# jsd_token_clip=0.05", "-# temperature=1.1", "-# lmbda=1", "-# max_completion_length=1024        # <= 4096", "-# fixed_teacher=true", "-# use_ema_teacher=false", "-# reason_first=false", "-# teacher_thinking=false", "+# The method itself lives in /workspace/repo (direction-preserving token-influence", "+# clipping + padding-hole-free position ids). The only knob set here is the LR", "+# schedule: the released OPSD recipe (train_baseline.sh / the reference run's logs)", "+# leaves lr_scheduler_type at the HF default, i.e. linear decay to 0 over the 100", "+# steps, which train_capped.sh would otherwise override to `constant`.", "+lr_scheduler_type=linear"]}], "originalFile": "# OPSD method recipe — whitelisted hyper-parameter overrides (KEY=VALUE per line).\n# Empty/all-commented == the OPSD baseline recipe on top of your /workspace/repo\n# code changes. The scorer re-trains with THIS file + your repo for 100 steps.\n# Budget knobs (max_steps, epochs, batch, gradient_accumulation, num_processes,\n# model) are FIXED by the scorer and ignored here.\n#\n# learning_rate=5e-6\n# lora_r=64\n# lora_alpha=128\n# beta=0\n# jsd_token_clip=0.05\n# temperature=1.1\n# lmbda=1\n# max_completion_length=1024        # <= 4096\n# fixed_teacher=true\n# use_ema_teacher=false\n# reason_first=false\n# teacher_thinking=false\n", "userModified": false}

#27

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashLaunch Exp1 training in background

args

{
  "command": "cd /workspace && rm -rf /workspace/runs/e1 && (SEED=42 OUT_DIR=/workspace/runs/e1 bash /workspace/train_capped.sh > /workspace/runs/e1_train.out 2>&1; echo \"TRAIN_RC=$?\" >> /workspace/runs/e1_train.out) ",
  "description": "Launch Exp1 training in background",
  "run_in_background": true
}

Bash result

Command running in background with ID: bnhy9yn5i. Output is being written to: /tmp/claude-0/-workspace/3476b637-41e1-4437-8c9a-f53302e59a44/tasks/bnhy9yn5i.output. You will be notified when it completes. To check interim output, use Read on that file path.
Session cwd remains /workspace/repo; directory changes made by the backgrounded command do not apply to subsequent commands.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "bnhy9yn5i", "backgroundCwdHint": "Session cwd remains /workspace/repo; directory changes made by the backgrounded command do not apply to subsequent commands."}

#28

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashMonitor Exp1 startup

args

{
  "command": "sleep 180; tail -c 1500 /workspace/runs/e1_train.out",
  "description": "Monitor Exp1 startup",
  "timeout": 200000
}

Bash result

68%|██████▊   | 19953/29434 [02:25<01:00, 155.64 examples/s]
Tokenizing train dataset:  68%|██████▊   | 19971/29434 [02:25<00:59, 158.37 examples/s]
Tokenizing train dataset:  68%|██████▊   | 19988/29434 [02:25<01:00, 156.97 examples/s]
Tokenizing train dataset:  68%|██████▊   | 20007/29434 [02:26<02:03, 76.22 examples/s] 
Tokenizing train dataset:  68%|██████▊   | 20021/29434 [02:26<01:51, 84.64 examples/s]
Tokenizing train dataset:  68%|██████▊   | 20036/29434 [02:26<01:38, 95.06 examples/s]
Tokenizing train dataset:  68%|██████▊   | 20051/29434 [02:26<01:30, 103.50 examples/s]
Tokenizing train dataset:  68%|██████▊   | 20068/29434 [02:26<01:21, 115.25 examples/s]
Tokenizing train dataset:  68%|██████▊   | 20084/29434 [02:26<01:15, 123.03 examples/s]
Tokenizing train dataset:  68%|██████▊   | 20105/29434 [02:26<01:13, 126.75 examples/s]
Tokenizing train dataset:  68%|██████▊   | 20122/29434 [02:26<01:09, 133.81 examples/s]
Tokenizing train dataset:  68%|██████▊   | 20137/29434 [02:26<01:08, 136.47 examples/s]
Tokenizing train dataset:  68%|██████▊   | 20154/29434 [02:27<01:04, 143.76 examples/s]
Tokenizing train dataset:  69%|██████▊   | 20170/29434 [02:27<01:04, 143.73 examples/s]
Tokenizing train dataset:  69%|██████▊   | 20194/29434 [02:27<01:02, 147.67 examples/s]

[stdout]
68%|██████▊   | 19953/29434 [02:25<01:00, 155.64 examples/s]
Tokenizing train dataset:  68%|██████▊   | 19971/29434 [02:25<00:59, 158.37 examples/s]
Tokenizing train dataset:  68%|██████▊   | 19988/29434 [02:25<01:00, 156.97 examples/s]
Tokenizing train dataset:  68%|██████▊   | 20007/29434 [02:26<02:03, 76.22 examples/s] 
Tokenizing train dataset:  68%|██████▊   | 20021/29434 [02:26<01:51, 84.64 examples/s]
Tokenizing train dataset:  68%|██████▊   | 20036/29434 [02:26<01:38, 95.06 examples/s]
Tokenizing train dataset:  68%|██████▊   | 20051/29434 [02:26<01:30, 103.50 examples/s]
Tokenizing train dataset:  68%|██████▊   | 20068/29434 [02:26<01:21, 115.25 examples/s]
Tokenizing train dataset:  68%|██████▊   | 20084/29434 [02:26<01:15, 123.03 examples/s]
Tokenizing train dataset:  68%|██████▊   | 20105/29434 [02:26<01:13, 126.75 examples/s]
Tokenizing train dataset:  68%|██████▊   | 20122/29434 [02:26<01:09, 133.81 examples/s]
Tokenizing train dataset:  68%|██████▊   | 20137/29434 [02:26<01:08, 136.47 examples/s]
Tokenizing train dataset:  68%|██████▊   | 20154/29434 [02:27<01:04, 143.76 examples/s]
Tokenizing train dataset:  69%|██████▊   | 20170/29434 [02:27<01:04, 143.73 examples/s]
Tokenizing train dataset:  69%|██████▊   | 20194/29434 [02:27<01:02, 147.67 examples/s]
[metadata] {"noOutputExpected": false}

#29

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashCheck first training steps

args

{
  "command": "sleep 240; grep -o \"{'loss'[^}]*}\" /workspace/runs/e1_train.out | tail -5; tail -c 300 /workspace/runs/e1_train.out",
  "description": "Check first training steps",
  "timeout": 260000
}

Bash result

{'loss': 0.0152, 'grad_norm': 0.17283257842063904, 'learning_rate': 4.95e-06, 'on_policy_loss': 0.0152, 'div_mean': 0.1381459180265665, 'div_saturated': 0.24109773710370064, 'epoch': 0.0}
{'loss': 0.0153, 'grad_norm': 0.1611056625843048, 'learning_rate': 4.85e-06, 'on_policy_loss': 0.0153, 'div_mean': 0.1426180936396122, 'div_saturated': 0.24665677919983864, 'epoch': 0.0}
{'loss': 0.0144, 'grad_norm': 0.14306330680847168, 'learning_rate': 4.75e-06, 'on_policy_loss': 0.0144, 'div_mean': 0.15777955949306488, 'div_saturated': 0.20100471004843712, 'epoch': 0.01}
{'loss': 0.0143, 'grad_norm': 0.1335732787847519, 'learning_rate': 4.65e-06, 'on_policy_loss': 0.0143, 'div_mean': 0.11887498199939728, 'div_saturated': 0.22756124287843704, 'epoch': 0.01}
prompts: 4, total tokens: 3641, avg length: 910.2, speed: 973.5 tok/s
vLLM generation done - elapsed time: 3.79s, prompts: 4, total tokens: 3821, avg length: 955.2, speed: 1008.8 tok/s
vLLM generation done - elapsed time: 3.78s, prompts: 4, total tokens: 3816, avg length: 954.0, speed: 1009.8 tok/s

[stdout]
{'loss': 0.0152, 'grad_norm': 0.17283257842063904, 'learning_rate': 4.95e-06, 'on_policy_loss': 0.0152, 'div_mean': 0.1381459180265665, 'div_saturated': 0.24109773710370064, 'epoch': 0.0}
{'loss': 0.0153, 'grad_norm': 0.1611056625843048, 'learning_rate': 4.85e-06, 'on_policy_loss': 0.0153, 'div_mean': 0.1426180936396122, 'div_saturated': 0.24665677919983864, 'epoch': 0.0}
{'loss': 0.0144, 'grad_norm': 0.14306330680847168, 'learning_rate': 4.75e-06, 'on_policy_loss': 0.0144, 'div_mean': 0.15777955949306488, 'div_saturated': 0.20100471004843712, 'epoch': 0.01}
{'loss': 0.0143, 'grad_norm': 0.1335732787847519, 'learning_rate': 4.65e-06, 'on_policy_loss': 0.0143, 'div_mean': 0.11887498199939728, 'div_saturated': 0.22756124287843704, 'epoch': 0.01}
prompts: 4, total tokens: 3641, avg length: 910.2, speed: 973.5 tok/s
vLLM generation done - elapsed time: 3.79s, prompts: 4, total tokens: 3821, avg length: 955.2, speed: 1008.8 tok/s
vLLM generation done - elapsed time: 3.78s, prompts: 4, total tokens: 3816, avg length: 954.0, speed: 1009.8 tok/s
[metadata] {"noOutputExpected": false}

#30

narration

While Exp1 trains, let me prepare the other levers behind env switches:

toolEdit

args

{
  "replace_all": false,
  "file_path": "/workspace/repo/opsd_trainer.py",
  "old_string": "FIX_POSITION_IDS = _env_flag(\"OPSD_FIX_POSITION_IDS\", True)",
  "new_string": "FIX_POSITION_IDS = _env_flag(\"OPSD_FIX_POSITION_IDS\", True)\n# Temperature used inside the distillation loss. `--temperature` is shared with the\n# rollout sampler, so this allows matching the two distributions at a temperature of\n# our choosing while still sampling at 1.1. 0 == follow --temperature.\nLOSS_TEMPERATURE = float(os.environ.get(\"OPSD_LOSS_TEMPERATURE\", \"0\") or 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": "FIX_POSITION_IDS = _env_flag(\"OPSD_FIX_POSITION_IDS\", True)", "newString": "FIX_POSITION_IDS = _env_flag(\"OPSD_FIX_POSITION_IDS\", True)\n# Temperature used inside the distillation loss. `--temperature` is shared with the\n# rollout sampler, so this allows matching the two distributions at a temperature of\n# our choosing while still sampling at 1.1. 0 == follow --temperature.\nLOSS_TEMPERATURE = float(os.environ.get(\"OPSD_LOSS_TEMPERATURE\", \"0\") or 0)", "originalFile": null, "structuredPatch": [{"oldStart": 99, "oldLines": 6, "newStart": 99, "newLines": 10, "lines": [" # on-policyness) and the teacher would score the completion as if a few hundred tokens", " # of context were missing.", " FIX_POSITION_IDS = _env_flag(\"OPSD_FIX_POSITION_IDS\", True)", "+# Temperature used inside the distillation loss. `--temperature` is shared with the", "+# rollout sampler, so this allows matching the two distributions at a temperature of", "+# our choosing while still sampling at 1.1. 0 == follow --temperature.", "+LOSS_TEMPERATURE = float(os.environ.get(\"OPSD_LOSS_TEMPERATURE\", \"0\") or 0)", " ", " ", " def _position_ids_from_mask(attention_mask):"]}], "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": "        self.lmbda = args.lmbda\n        self.beta = args.beta\n        self.temperature = args.temperature",
  "new_string": "        self.lmbda = args.lmbda\n        self.beta = args.beta\n        self.temperature = args.temperature\n        self.loss_temperature = LOSS_TEMPERATURE or args.temperature"
}

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.lmbda = args.lmbda\n        self.beta = args.beta\n        self.temperature = args.temperature", "newString": "        self.lmbda = args.lmbda\n        self.beta = args.beta\n        self.temperature = args.temperature\n        self.loss_temperature = LOSS_TEMPERATURE or args.temperature", "originalFile": null, "structuredPatch": [{"oldStart": 212, "oldLines": 6, "newStart": 212, "newLines": 7, "lines": ["         self.lmbda = args.lmbda", "         self.beta = args.beta", "         self.temperature = args.temperature", "+        self.loss_temperature = LOSS_TEMPERATURE or args.temperature", "         self.top_p = args.top_p", "         self.seq_kd = args.seq_kd", "         self.use_thinking_machines_loss = use_thinking_machines_loss"]}], "userModified": false, "replaceAll": false}

#32

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashFind temperature uses

args

{
  "command": "grep -n \"self.temperature\" opsd_trainer.py",
  "description": "Find temperature uses"
}

Bash result

214:        self.temperature = args.temperature
724:            student_log_probs = F.log_softmax(student_logits / self.temperature, dim=-1)
768:                teacher_log_probs = F.log_softmax(teacher_logits / self.temperature, dim=-1)
818:                temperature=self.temperature,  # Let the function handle temperature

[stdout]
214:        self.temperature = args.temperature
724:            student_log_probs = F.log_softmax(student_logits / self.temperature, dim=-1)
768:                teacher_log_probs = F.log_softmax(teacher_logits / self.temperature, dim=-1)
818:                temperature=self.temperature,  # Let the function handle temperature
[metadata] {"noOutputExpected": false}

#33

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashUse loss_temperature in the loss

args

{
  "command": "python - <<'EOF'\nimport re\np='opsd_trainer.py'; s=open(p).read()\ns=s.replace(\"\"\"            student_log_probs = F.log_softmax(student_logits / self.temperature, dim=-1)\"\"\",\"\"\"            student_log_probs = F.log_softmax(student_logits / self.loss_temperature, dim=-1)\"\"\")\ns=s.replace(\"\"\"                teacher_log_probs = F.log_softmax(teacher_logits / self.temperature, dim=-1)\"\"\",\"\"\"                teacher_log_probs = F.log_softmax(teacher_logits / self.loss_temperature, dim=-1)\"\"\")\ns=s.replace(\"\"\"                temperature=self.temperature,  # Let the function handle temperature\"\"\",\"\"\"                temperature=self.loss_temperature,  # Let the function handle temperature\"\"\")\nopen(p,'w').write(s)\nEOF\ngrep -n \"loss_temperature\" opsd_trainer.py",
  "description": "Use loss_temperature in the loss"
}

Bash result

215:        self.loss_temperature = LOSS_TEMPERATURE or args.temperature
724:            student_log_probs = F.log_softmax(student_logits / self.loss_temperature, dim=-1)
768:                teacher_log_probs = F.log_softmax(teacher_logits / self.loss_temperature, dim=-1)
818:                temperature=self.loss_temperature,  # Let the function handle temperature

[stdout]
215:        self.loss_temperature = LOSS_TEMPERATURE or args.temperature
724:            student_log_probs = F.log_softmax(student_logits / self.loss_temperature, dim=-1)
768:                teacher_log_probs = F.log_softmax(teacher_logits / self.loss_temperature, dim=-1)
818:                temperature=self.loss_temperature,  # Let the function handle temperature
[metadata] {"noOutputExpected": false}

#34

narration

Now the data-selection and teacher-mode levers in opsd_train.py:

toolEdit

args

{
  "replace_all": false,
  "file_path": "/workspace/repo/opsd_train.py",
  "old_string": "    dataset = load_dataset(\"siyanzhao/Openthoughts_math_30k_opsd\")\n    train_dataset = dataset[\"train\"]",
  "new_string": "    dataset = load_dataset(\"siyanzhao/Openthoughts_math_30k_opsd\")\n    train_dataset = dataset[\"train\"]\n\n    # ---- optional target-aligned data selection (see README table: AIME-style,\n    # short-numeric-answer competition problems) ----\n    data_filter = os.environ.get(\"OPSD_DATA_FILTER\", \"none\")\n    if data_filter != \"none\":\n        import re as _re\n\n        n_before = len(train_dataset)\n        if data_filter == \"numeric\":\n            keep = lambda ex: bool(_re.fullmatch(r\"-?\\d{1,6}\", (ex[\"Answer\"] or \"\").strip()))\n        elif data_filter == \"hard_numeric\":\n            keep = lambda ex: (\n                bool(_re.fullmatch(r\"-?\\d{1,6}\", (ex[\"Answer\"] or \"\").strip()))\n                and ex[\"generated_token_count\"] >= 2000\n            )\n        else:\n            raise ValueError(f\"unknown OPSD_DATA_FILTER={data_filter}\")\n        train_dataset = train_dataset.filter(keep)\n        print(f\"[data] filter={data_filter}: {n_before} -> {len(train_dataset)} examples\")\n\n    teacher_thinking = script_args.teacher_thinking\n    if \"OPSD_TEACHER_THINKING\" in os.environ:\n        teacher_thinking = os.environ[\"OPSD_TEACHER_THINKING\"].lower() in (\"1\", \"true\", \"yes\")\n    student_thinking = script_args.student_thinking\n    if \"OPSD_STUDENT_THINKING\" in os.environ:\n        student_thinking = os.environ[\"OPSD_STUDENT_THINKING\"].lower() in (\"1\", \"true\", \"yes\")\n    print(f\"[modes] teacher_thinking={teacher_thinking} student_thinking={student_thinking}\")"
}

Edit result

The file /workspace/repo/opsd_train.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/workspace/repo/opsd_train.py", "oldString": "    dataset = load_dataset(\"siyanzhao/Openthoughts_math_30k_opsd\")\n    train_dataset = dataset[\"train\"]", "newString": "    dataset = load_dataset(\"siyanzhao/Openthoughts_math_30k_opsd\")\n    train_dataset = dataset[\"train\"]\n\n    # ---- optional target-aligned data selection (see README table: AIME-style,\n    # short-numeric-answer competition problems) ----\n    data_filter = os.environ.get(\"OPSD_DATA_FILTER\", \"none\")\n    if data_filter != \"none\":\n        import re as _re\n\n        n_before = len(train_dataset)\n        if data_filter == \"numeric\":\n            keep = lambda ex: bool(_re.fullmatch(r\"-?\\d{1,6}\", (ex[\"Answer\"] or \"\").strip()))\n        elif data_filter == \"hard_numeric\":\n            keep = lambda ex: (\n                bool(_re.fullmatch(r\"-?\\d{1,6}\", (ex[\"Answer\"] or \"\").strip()))\n                and ex[\"generated_token_count\"] >= 2000\n            )\n        else:\n            raise ValueError(f\"unknown OPSD_DATA_FILTER={data_filter}\")\n        train_dataset = train_dataset.filter(keep)\n        print(f\"[data] filter={data_filter}: {n_before} -> {len(train_dataset)} examples\")\n\n    teacher_thinking = script_args.teacher_thinking\n    if \"OPSD_TEACHER_THINKING\" in os.environ:\n        teacher_thinking = os.environ[\"OPSD_TEACHER_THINKING\"].lower() in (\"1\", \"true\", \"yes\")\n    student_thinking = script_args.student_thinking\n    if \"OPSD_STUDENT_THINKING\" in os.environ:\n        student_thinking = os.environ[\"OPSD_STUDENT_THINKING\"].lower() in (\"1\", \"true\", \"yes\")\n    print(f\"[modes] teacher_thinking={teacher_thinking} student_thinking={student_thinking}\")", "originalFile": null, "structuredPatch": [{"oldStart": 266, "oldLines": 6, "newStart": 266, "newLines": 33, "lines": ["     dataset = load_dataset(\"siyanzhao/Openthoughts_math_30k_opsd\")", "     train_dataset = dataset[\"train\"]", " ", "+    # ---- optional target-aligned data selection (see README table: AIME-style,", "+    # short-numeric-answer competition problems) ----", "+    data_filter = os.environ.get(\"OPSD_DATA_FILTER\", \"none\")", "+    if data_filter != \"none\":", "+        import re as _re", "+", "+        n_before = len(train_dataset)", "+        if data_filter == \"numeric\":", "+            keep = lambda ex: bool(_re.fullmatch(r\"-?\\d{1,6}\", (ex[\"Answer\"] or \"\").strip()))", "+        elif data_filter == \"hard_numeric\":", "+            keep = lambda ex: (", "+                bool(_re.fullmatch(r\"-?\\d{1,6}\", (ex[\"Answer\"] or \"\").strip()))", "+                and ex[\"generated_token_count\"] >= 2000", "+            )", "+        else:", "+            raise ValueError(f\"unknown OPSD_DATA_FILTER={data_filter}\")", "+        train_dataset = train_dataset.filter(keep)", "+        print(f\"[data] filter={data_filter}: {n_before} -> {len(train_dataset)} examples\")", "+", "+    teacher_thinking = script_args.teacher_thinking", "+    if \"OPSD_TEACHER_THINKING\" in os.environ:", "+        teacher_thinking = os.environ[\"OPSD_TEACHER_THINKING\"].lower() in (\"1\", \"true\", \"yes\")", "+    student_thinking = script_args.student_thinking", "+    if \"OPSD_STUDENT_THINKING\" in os.environ:", "+        student_thinking = os.environ[\"OPSD_STUDENT_THINKING\"].lower() in (\"1\", \"true\", \"yes\")", "+    print(f\"[modes] teacher_thinking={teacher_thinking} student_thinking={student_thinking}\")", "+", "     trainer = OPSDTrainer(", "         model=model_args.model_name_or_path,", "         args=training_args,"]}], "userModified": false, "replaceAll": false}

#35

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolEdit

args

{
  "replace_all": false,
  "file_path": "/workspace/repo/opsd_train.py",
  "old_string": "        student_thinking=script_args.student_thinking,\n        teacher_thinking=script_args.teacher_thinking,\n    )",
  "new_string": "        student_thinking=student_thinking,\n        teacher_thinking=teacher_thinking,\n    )"
}

Edit result

The file /workspace/repo/opsd_train.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/workspace/repo/opsd_train.py", "oldString": "        student_thinking=script_args.student_thinking,\n        teacher_thinking=script_args.teacher_thinking,\n    )", "newString": "        student_thinking=student_thinking,\n        teacher_thinking=teacher_thinking,\n    )", "originalFile": null, "structuredPatch": [{"oldStart": 307, "oldLines": 8, "newStart": 307, "newLines": 8, "lines": ["         jsd_token_clip=script_args.jsd_token_clip if script_args.jsd_token_clip > 0 else None,", "         use_ema_teacher=script_args.use_ema_teacher,", "         ema_decay=script_args.ema_decay,", "-        student_thinking=script_args.student_thinking,", "-        teacher_thinking=script_args.teacher_thinking,", "+        student_thinking=student_thinking,", "+        teacher_thinking=teacher_thinking,", "     )", " ", "     if training_args.eval_strategy != \"no\":"]}], "userModified": false, "replaceAll": false}

#36

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashCheck Exp1 progress

args

{
  "command": "grep -o \"{'loss'[^}]*}\" /workspace/runs/e1_train.out | tail -3; tail -c 200 /workspace/runs/e1_train.out; cat /workspace/.timer/remaining_secs",
  "description": "Check Exp1 progress"
}

Bash result

{'loss': 0.0129, 'grad_norm': 0.09583724290132523, 'learning_rate': 4.450000000000001e-06, 'on_policy_loss': 0.0129, 'div_mean': 0.09304866194725037, 'div_saturated': 0.16658934950828552, 'epoch': 0.01}
{'loss': 0.0129, 'grad_norm': 0.08715789765119553, 'learning_rate': 4.350000000000001e-06, 'on_policy_loss': 0.0129, 'div_mean': 0.10166295990347862, 'div_saturated': 0.19800014421343803, 'epoch': 0.02}
{'loss': 0.0131, 'grad_norm': 0.07616446912288666, 'learning_rate': 4.25e-06, 'on_policy_loss': 0.0131, 'div_mean': 0.08430289290845394, 'div_saturated': 0.18483004719018936, 'epoch': 0.02}
gth: 886.2, speed: 947.7 tok/s
vLLM generation done - elapsed time: 3.77s, prompts: 4, total tokens: 4096, avg length: 1024.0, speed: 1086.7 tok/s

 17%|█▋        | 17/100 [02:31<12:19,  8.91s/it]20428

[stdout]
{'loss': 0.0129, 'grad_norm': 0.09583724290132523, 'learning_rate': 4.450000000000001e-06, 'on_policy_loss': 0.0129, 'div_mean': 0.09304866194725037, 'div_saturated': 0.16658934950828552, 'epoch': 0.01}
{'loss': 0.0129, 'grad_norm': 0.08715789765119553, 'learning_rate': 4.350000000000001e-06, 'on_policy_loss': 0.0129, 'div_mean': 0.10166295990347862, 'div_saturated': 0.19800014421343803, 'epoch': 0.02}
{'loss': 0.0131, 'grad_norm': 0.07616446912288666, 'learning_rate': 4.25e-06, 'on_policy_loss': 0.0131, 'div_mean': 0.08430289290845394, 'div_saturated': 0.18483004719018936, 'epoch': 0.02}
gth: 886.2, speed: 947.7 tok/s
vLLM generation done - elapsed time: 3.77s, prompts: 4, total tokens: 4096, avg length: 1024.0, speed: 1086.7 tok/s

 17%|█▋        | 17/100 [02:31<12:19,  8.91s/it]20428
[metadata] {"noOutputExpected": false}

#37

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolWrite

args

{
  "file_path": "/workspace/cycle.sh",
  "content": "#!/bin/bash\n# dev helper: train (100 capped steps, seed 42) then full official-settings eval.\n# usage: NAME=e2 [env overrides...] bash /workspace/cycle.sh\nset -uo pipefail\nNAME=${NAME:?NAME required}\nSEED=${SEED:-42}\nOUT=/workspace/runs/$NAME\nrm -rf \"$OUT\"\necho \"=== [$NAME] train start $(date +%H:%M:%S)  (remaining $(cat /workspace/.timer/remaining_secs)s)\"\nSEED=$SEED OUT_DIR=$OUT PORT=${PORT:-12950} bash /workspace/train_capped.sh > /workspace/runs/${NAME}_train.out 2>&1\necho \"=== [$NAME] train rc=$? $(date +%H:%M:%S)\"\nCKPT=$OUT/capped_seed${SEED}/checkpoint-100\nls -d \"$CKPT\" || { echo \"[$NAME] NO CHECKPOINT\"; exit 4; }\necho \"=== [$NAME] eval start $(date +%H:%M:%S)\"\nbash /workspace/dev_eval.sh \"$CKPT\" /workspace/eval_results/${NAME}.json > /workspace/runs/${NAME}_eval.out 2>&1\necho \"=== [$NAME] eval rc=$? $(date +%H:%M:%S)\"\npython - <<EOF\nimport json\nr=json.load(open(\"/workspace/eval_results/${NAME}.json\"))\nprint(\"RESULT ${NAME}: avg@12=%.2f pass@12=%.2f fmt=%.1f\" % (r[\"average_at_n_pct\"], r[\"pass_at_n_pct\"], r[\"format_rate_pct\"]))\nEOF\necho \"=== [$NAME] done $(date +%H:%M:%S) (remaining $(cat /workspace/.timer/remaining_secs)s)\"\n"
}

Write result

File created successfully at: /workspace/cycle.sh (file state is current in your context — no need to Read it back)

[metadata] {"type": "create", "filePath": "/workspace/cycle.sh", "content": "#!/bin/bash\n# dev helper: train (100 capped steps, seed 42) then full official-settings eval.\n# usage: NAME=e2 [env overrides...] bash /workspace/cycle.sh\nset -uo pipefail\nNAME=${NAME:?NAME required}\nSEED=${SEED:-42}\nOUT=/workspace/runs/$NAME\nrm -rf \"$OUT\"\necho \"=== [$NAME] train start $(date +%H:%M:%S)  (remaining $(cat /workspace/.timer/remaining_secs)s)\"\nSEED=$SEED OUT_DIR=$OUT PORT=${PORT:-12950} bash /workspace/train_capped.sh > /workspace/runs/${NAME}_train.out 2>&1\necho \"=== [$NAME] train rc=$? $(date +%H:%M:%S)\"\nCKPT=$OUT/capped_seed${SEED}/checkpoint-100\nls -d \"$CKPT\" || { echo \"[$NAME] NO CHECKPOINT\"; exit 4; }\necho \"=== [$NAME] eval start $(date +%H:%M:%S)\"\nbash /workspace/dev_eval.sh \"$CKPT\" /workspace/eval_results/${NAME}.json > /workspace/runs/${NAME}_eval.out 2>&1\necho \"=== [$NAME] eval rc=$? $(date +%H:%M:%S)\"\npython - <<EOF\nimport json\nr=json.load(open(\"/workspace/eval_results/${NAME}.json\"))\nprint(\"RESULT ${NAME}: avg@12=%.2f pass@12=%.2f fmt=%.1f\" % (r[\"average_at_n_pct\"], r[\"pass_at_n_pct\"], r[\"format_rate_pct\"]))\nEOF\necho \"=== [$NAME] done $(date +%H:%M:%S) (remaining $(cat /workspace/.timer/remaining_secs)s)\"\n", "structuredPatch": [], "originalFile": null, "userModified": false}

#38

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashPoll Exp1

args

{
  "command": "grep -o \"{'loss'[^}]*}\" /workspace/runs/e1_train.out | tail -2; tail -c 120 /workspace/runs/e1_train.out",
  "description": "Poll Exp1"
}

Bash result

{'loss': 0.0133, 'grad_norm': 0.07130550593137741, 'learning_rate': 4.05e-06, 'on_policy_loss': 0.0133, 'div_mean': 0.07669250760227442, 'div_saturated': 0.18804431706666946, 'epoch': 0.02}
{'loss': 0.0131, 'grad_norm': 0.07268676906824112, 'learning_rate': 3.95e-06, 'on_policy_loss': 0.0131, 'div_mean': 0.08434001356363297, 'div_saturated': 0.19104798138141632, 'epoch': 0.02}
ts: 4, total tokens: 3522, avg length: 880.5, speed: 937.5 tok/s

 23%|██▎       | 23/100 [03:25<11:24,  8.89s/it]

[stdout]
{'loss': 0.0133, 'grad_norm': 0.07130550593137741, 'learning_rate': 4.05e-06, 'on_policy_loss': 0.0133, 'div_mean': 0.07669250760227442, 'div_saturated': 0.18804431706666946, 'epoch': 0.02}
{'loss': 0.0131, 'grad_norm': 0.07268676906824112, 'learning_rate': 3.95e-06, 'on_policy_loss': 0.0131, 'div_mean': 0.08434001356363297, 'div_saturated': 0.19104798138141632, 'epoch': 0.02}
ts: 4, total tokens: 3522, avg length: 880.5, speed: 937.5 tok/s

 23%|██▎       | 23/100 [03:25<11:24,  8.89s/it]
[metadata] {"noOutputExpected": false}

#39

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashChain Exp1 eval after training

args

{
  "command": "cd /workspace && (while ! grep -q \"TRAIN_RC=\" /workspace/runs/e1_train.out; do sleep 20; done; sleep 5; bash /workspace/dev_eval.sh /workspace/runs/e1/capped_seed42/checkpoint-100 /workspace/eval_results/e1.json > /workspace/runs/e1_eval.out 2>&1; echo EVAL_RC=$? >> /workspace/runs/e1_eval.out) ",
  "description": "Chain Exp1 eval after training",
  "run_in_background": true
}

Bash result

Command running in background with ID: bo4el928g. Output is being written to: /tmp/claude-0/-workspace/3476b637-41e1-4437-8c9a-f53302e59a44/tasks/bo4el928g.output. You will be notified when it completes. To check interim output, use Read on that file path.
Session cwd remains /workspace/repo; directory changes made by the backgrounded command do not apply to subsequent commands.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "bo4el928g", "backgroundCwdHint": "Session cwd remains /workspace/repo; directory changes made by the backgrounded command do not apply to subsequent commands."}

#40

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashWait for Exp1 training to finish

args

{
  "command": "sleep 720; grep -o \"{'loss'[^}]*}\" /workspace/runs/e1_train.out | tail -2; tail -c 200 /workspace/runs/e1_train.out; echo; tail -c 300 /workspace/runs/e1_eval.out 2>/dev/null; cat /workspace/.timer/remaining_secs",
  "description": "Wait for Exp1 training to finish",
  "timeout": 740000
}

Bash result · error

Exit code 143
Command timed out after 10m 0s

[error] tool reported failure

#41

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashPoll Exp1 status

args

{
  "command": "grep -o \"{'loss'[^}]*}\" /workspace/runs/e1_train.out | tail -2; tail -c 200 /workspace/runs/e1_train.out; echo; tail -c 400 /workspace/runs/e1_eval.out 2>/dev/null; cat /workspace/.timer/remaining_secs",
  "description": "Poll Exp1 status"
}

Bash result

{'loss': 0.0111, 'grad_norm': 0.03491154685616493, 'learning_rate': 5.5e-07, 'on_policy_loss': 0.0111, 'div_mean': 0.06253112945705652, 'div_saturated': 0.14302151463925838, 'epoch': 0.1}
{'loss': 0.0119, 'grad_norm': 0.0501994825899601, 'learning_rate': 4.5000000000000003e-07, 'on_policy_loss': 0.0119, 'div_mean': 0.09213345125317574, 'div_saturated': 0.19014500454068184, 'epoch': 0.1}
 to $ x $

Recall that $ \log_2 x = 4 $ means:

$$
x = 2^4 = 16
$$

---

### Final Answer

$$
\boxed{16}
$$<|im_end|>
================================================================================


19757

[stdout]
{'loss': 0.0111, 'grad_norm': 0.03491154685616493, 'learning_rate': 5.5e-07, 'on_policy_loss': 0.0111, 'div_mean': 0.06253112945705652, 'div_saturated': 0.14302151463925838, 'epoch': 0.1}
{'loss': 0.0119, 'grad_norm': 0.0501994825899601, 'learning_rate': 4.5000000000000003e-07, 'on_policy_loss': 0.0119, 'div_mean': 0.09213345125317574, 'div_saturated': 0.19014500454068184, 'epoch': 0.1}
 to $ x $

Recall that $ \log_2 x = 4 $ means:

$$
x = 2^4 = 16
$$

---

### Final Answer

$$
\boxed{16}
$$<|im_end|>
================================================================================


19757
[metadata] {"noOutputExpected": false}

#42

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashPoll Exp1 eval

args

{
  "command": "sleep 540; tail -3 /workspace/runs/e1_eval.out; ls -la /workspace/eval_results/; cat /workspace/.timer/remaining_secs",
  "description": "Poll Exp1 eval",
  "timeout": 560000
}

Bash result

Processed prompts:   0%|          | 0/96 [00:00<?, ?it/s, est. speed input: 0.00 toks/s, output: 0.00 toks/s]INFO 08-03 04:21:09 [llm.py:306] Supported_tasks: ['generate']

Adding requests:   0%|          | 0/8 [00:00<?, ?it/s]
Adding requests: 100%|██████████| 8/8 [00:00<00:00, 289.22it/s]

Processed prompts:   0%|          | 0/96 [00:00<?, ?it/s, est. speed input: 0.00 toks/s, output: 0.00 toks/s]
Processed prompts:  12%|█▎        | 12/96 [02:58<20:52, 14.91s/it, est. speed input: 10.46 toks/s, output: 400.88 toks/s]
Processed prompts:  14%|█▍        | 12/84 [04:29<26:54, 22.43s/it, est. speed input: 3.43 toks/s, output: 420.20 toks/s]
Processed prompts:  12%|█▎        | 12/96 [04:32<31:48, 22.72s/it, est. speed input: 6.52 toks/s, output: 341.75 toks/s]
Processed prompts:  25%|██▌       | 24/96 [04:36<13:06, 10.92s/it, est. speed input: 14.02 toks/s, output: 567.83 toks/s]
Processed prompts:  29%|██▊       | 24/84 [05:05<11:00, 11.00s/it, est. speed input: 6.29 toks/s, output: 780.23 toks/s]
Processed prompts:  38%|███▊      | 36/96 [06:23<10:00, 10.00s/it, est. speed input: 12.33 toks/s, output: 730.90 toks/s]total 1
drwxr-xr-x 1 root root  10 Aug  2 17:15 .
drwxr-xr-x 1 root root 240 Aug  3 04:08 ..
19146

[stdout]

Processed prompts:   0%|          | 0/96 [00:00<?, ?it/s, est. speed input: 0.00 toks/s, output: 0.00 toks/s]INFO 08-03 04:21:09 [llm.py:306] Supported_tasks: ['generate']

Adding requests:   0%|          | 0/8 [00:00<?, ?it/s]
Adding requests: 100%|██████████| 8/8 [00:00<00:00, 289.22it/s]

Processed prompts:   0%|          | 0/96 [00:00<?, ?it/s, est. speed input: 0.00 toks/s, output: 0.00 toks/s]
Processed prompts:  12%|█▎        | 12/96 [02:58<20:52, 14.91s/it, est. speed input: 10.46 toks/s, output: 400.88 toks/s]
Processed prompts:  14%|█▍        | 12/84 [04:29<26:54, 22.43s/it, est. speed input: 3.43 toks/s, output: 420.20 toks/s]
Processed prompts:  12%|█▎        | 12/96 [04:32<31:48, 22.72s/it, est. speed input: 6.52 toks/s, output: 341.75 toks/s]
Processed prompts:  25%|██▌       | 24/96 [04:36<13:06, 10.92s/it, est. speed input: 14.02 toks/s, output: 567.83 toks/s]
Processed prompts:  29%|██▊       | 24/84 [05:05<11:00, 11.00s/it, est. speed input: 6.29 toks/s, output: 780.23 toks/s]
Processed prompts:  38%|███▊      | 36/96 [06:23<10:00, 10.00s/it, est. speed input: 12.33 toks/s, output: 730.90 toks/s]total 1
drwxr-xr-x 1 root root  10 Aug  2 17:15 .
drwxr-xr-x 1 root root 240 Aug  3 04:08 ..
19146
[metadata] {"noOutputExpected": false}

#43

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashGet Exp1 result

args

{
  "command": "sleep 600; grep -o \"RESULT\\|EVAL_RC=.\" /workspace/runs/e1_eval.out|tail -2; python -c \"\nimport json;r=json.load(open('/workspace/eval_results/e1.json'));print('avg@12=%.2f pass=%.2f fmt=%.2f'%(r['average_at_n_pct'],r['pass_at_n_pct'],r['format_rate_pct']))\" 2>/dev/null || tail -c 400 /workspace/runs/e1_eval.out; cat /workspace/.timer/remaining_secs",
  "description": "Get Exp1 result",
  "timeout": 620000
}

Bash result · error

Exit code 143
Command timed out after 10m 0s

[error] tool reported failure

#44

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashCheck Exp1 result

args

{
  "command": "python -c \"\nimport json;r=json.load(open('/workspace/eval_results/e1.json'));print('avg@12=%.2f pass=%.2f fmt=%.2f'%(r['average_at_n_pct'],r['pass_at_n_pct'],r['format_rate_pct']))\" 2>/dev/null || tail -c 300 /workspace/runs/e1_eval.out; cat /workspace/.timer/remaining_secs",
  "description": "Check Exp1 result"
}

Bash result

s, output: 1592.55 toks/s]
Processed prompts: 100%|██████████| 84/84 [17:18<00:00, 12.36s/it, est. speed input: 9.58 toks/s, output: 1592.55 toks/s]

Processed prompts:  50%|█████     | 48/96 [17:53<15:37, 19.53s/it, est. speed input: 6.37 toks/s, output: 848.90 toks/s]18535

[stdout]
s, output: 1592.55 toks/s]
Processed prompts: 100%|██████████| 84/84 [17:18<00:00, 12.36s/it, est. speed input: 9.58 toks/s, output: 1592.55 toks/s]

Processed prompts:  50%|█████     | 48/96 [17:53<15:37, 19.53s/it, est. speed input: 6.37 toks/s, output: 848.90 toks/s]18535
[metadata] {"noOutputExpected": false}

#45

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashCheck Exp1 result again

args

{
  "command": "sleep 570; python -c \"\nimport json;r=json.load(open('/workspace/eval_results/e1.json'));print('avg@12=%.2f pass=%.2f fmt=%.2f'%(r['average_at_n_pct'],r['pass_at_n_pct'],r['format_rate_pct']))\" 2>/dev/null || tail -c 200 /workspace/runs/e1_eval.out; cat /workspace/.timer/remaining_secs",
  "description": "Check Exp1 result again",
  "timeout": 590000
}

Bash result

avg@12=47.50 pass=76.67 fmt=99.72
17954

[stdout]
avg@12=47.50 pass=76.67 fmt=99.72
17954
[metadata] {"noOutputExpected": false}

#46

narration

Exp1 = 47.50 (vs baseline 54.22) — a large, clearly-real regression. That's very informative: making the loss faithfully match the privileged teacher hurts. Let me record this and pivot.

toolBashSummarize Exp1

args

{
  "command": "cd /workspace && python -c \"\nimport json\nr=json.load(open('/workspace/eval_results/e1.json'))\nprint({k:r[k] for k in ('average_at_n_pct','pass_at_n_pct','format_rate_pct','total_correct')})\n\"; grep -o \"{'loss'[^}]*}\" /workspace/runs/e1_train.out | tail -1",
  "description": "Summarize Exp1"
}

Bash result

{'average_at_n_pct': 47.5, 'pass_at_n_pct': 76.66666666666667, 'format_rate_pct': 99.72222222222223, 'total_correct': 171}
{'loss': 0.0118, 'grad_norm': 0.048875998705625534, 'learning_rate': 5.0000000000000004e-08, 'on_policy_loss': 0.0118, 'div_mean': 0.05651395954191685, 'div_saturated': 0.13712003827095032, 'epoch': 0.11}

[stdout]
{'average_at_n_pct': 47.5, 'pass_at_n_pct': 76.66666666666667, 'format_rate_pct': 99.72222222222223, 'total_correct': 171}
{'loss': 0.0118, 'grad_norm': 0.048875998705625534, 'learning_rate': 5.0000000000000004e-08, 'on_policy_loss': 0.0118, 'div_mean': 0.05651395954191685, 'div_saturated': 0.13712003827095032, 'epoch': 0.11}
[metadata] {"noOutputExpected": false}

#47

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashCheck timer and trainer env defaults

args

{
  "command": "cat .timer/remaining_secs && grep -n \"OPSD_CLIP_MODE\\|FIX_POSITION_IDS\\|LOSS_TEMPERATURE\" repo/opsd_trainer.py | head -20",
  "description": "Check timer and trainer env defaults"
}

Bash result

17674
94:CLIP_MODE = os.environ.get("OPSD_CLIP_MODE", "token")
101:FIX_POSITION_IDS = _env_flag("OPSD_FIX_POSITION_IDS", True)
105:LOSS_TEMPERATURE = float(os.environ.get("OPSD_LOSS_TEMPERATURE", "0") or 0)
110:    if not FIX_POSITION_IDS or attention_mask is None:
215:        self.loss_temperature = LOSS_TEMPERATURE or args.temperature

[stdout]
17674
94:CLIP_MODE = os.environ.get("OPSD_CLIP_MODE", "token")
101:FIX_POSITION_IDS = _env_flag("OPSD_FIX_POSITION_IDS", True)
105:LOSS_TEMPERATURE = float(os.environ.get("OPSD_LOSS_TEMPERATURE", "0") or 0)
110:    if not FIX_POSITION_IDS or attention_mask is None:
215:        self.loss_temperature = LOSS_TEMPERATURE or args.temperature
[metadata] {"noOutputExpected": 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": "CLIP_MODE = os.environ.get(\"OPSD_CLIP_MODE\", \"token\")",
  "new_string": "CLIP_MODE = os.environ.get(\"OPSD_CLIP_MODE\", \"element\")"
}

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": "CLIP_MODE = os.environ.get(\"OPSD_CLIP_MODE\", \"token\")", "newString": "CLIP_MODE = os.environ.get(\"OPSD_CLIP_MODE\", \"element\")", "originalFile": null, "structuredPatch": [{"oldStart": 91, "oldLines": 7, "newStart": 91, "newLines": 7, "lines": [" ", " # How `jsd_token_clip` bounds a token's contribution: \"token\" (direction-preserving,", " # default) or \"element\" (the original per-vocabulary-entry clamp).", "-CLIP_MODE = os.environ.get(\"OPSD_CLIP_MODE\", \"token\")", "+CLIP_MODE = os.environ.get(\"OPSD_CLIP_MODE\", \"element\")", " # Feed position_ids derived from the attention mask instead of letting them default to", " # arange(seq_len). Prompts are right-padded, so the default leaves a positional hole", " # between the prompt and the completion for every example shorter than the batch max:"]}], "userModified": false, "replaceAll": false}

#49

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashConfirm recipe.env is consumed

args

{
  "command": "grep -n \"recipe.env\\|RECIPE\" /workspace/train_capped.sh | head",
  "description": "Confirm recipe.env is consumed"
}

Bash result

17:# Method hyper-parameters come from recipe.env (KEY=VALUE, one per line). Only
28:RECIPE="${RECIPE:-/workspace/submission/recipe.env}"
48:# ---- overlay whitelisted knobs from recipe.env (budget/unknown keys ignored) ----
49:if [ -f "$RECIPE" ]; then
54:  done < "$RECIPE"

[stdout]
17:# Method hyper-parameters come from recipe.env (KEY=VALUE, one per line). Only
28:RECIPE="${RECIPE:-/workspace/submission/recipe.env}"
48:# ---- overlay whitelisted knobs from recipe.env (budget/unknown keys ignored) ----
49:if [ -f "$RECIPE" ]; then
54:  done < "$RECIPE"
[metadata] {"noOutputExpected": false}

#50

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashLaunch Exp2 train+eval cycle

args

{
  "command": "NAME=e2 OPSD_CLIP_MODE=element OPSD_FIX_POSITION_IDS=1 OPSD_TEACHER_THINKING=0 nohup bash cycle.sh > /workspace/runs/e2_cycle.log 2>&1 &\nsleep 20; cat /workspace/runs/e2_cycle.log",
  "description": "Launch Exp2 train+eval cycle"
}

Bash result

=== [e2] train start 04:54:00  (remaining 17654s)

[stdout]
=== [e2] train start 04:54:00  (remaining 17654s)
[metadata] {"noOutputExpected": false}