on-policy-self-distillation
gpt-5.6-sol
on-policy-self-distillation — graded by the task's verifier against a measured baseline.
- backend
- modal
- variant
- codex azure_ai/gpt-5.6-sol
- metric
- AIME24 avg@12 accuracy
- actions
- 636
- duration
- 8h 10m
- recorded
- 2026-08-03
run idopsd-aime24-4gpus__3tyLi4e
Everything gpt-5.6-sol did on this task, in order.
download JSON ↗codexgpt-5.6-sol683 turns47 narration636 reasoning636 action636 observation1 final
#26
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolexec
args
{
"input": "const patch = \"*** Begin Patch\\n*** Update File: /workspace/repo/data_collator.py\\n@@\\n teacher_reasoning_prompts = [] # NEW: for reason_first mode\\n+ answer_texts = []\\n@@\\n solution = feature[\\\"solution\\\"]\\n+ answer_texts.append(str(feature.get(\\\"Answer\\\", \\\"\\\")))\\n@@\\n result = {\\n@@\\n \\\"student_prompt_lengths_per_example\\\": torch.tensor(student_prompt_lengths),\\n+ # Kept as Python strings until training_step grades the sampled rollout.\\n+ \\\"answer_texts\\\": answer_texts,\\n }\\n*** Update File: /workspace/repo/opsd_train.py\\n@@\\n import os\\n+import re\\n import wandb\\n@@\\n dataset = load_dataset(\\\"siyanzhao/Openthoughts_math_30k_opsd\\\")\\n train_dataset = dataset[\\\"train\\\"]\\n+\\n+ # Frontier curriculum for AIME-style reasoning. OPSD only has 3,200 examples of\\n+ # budget, so spending them on elementary MATH/AMC items gives the privileged\\n+ # teacher little useful disagreement. Select hard olympiad/AoPS questions with\\n+ # direct integer answers (AIME's answer regime). Dataset.select is an in-memory\\n+ # view and does not create or alter dataset cache files.\\n+ def is_aime_style_answer(answer):\\n+ answer = re.sub(r\\\"\\\\\\\\(?:text|mathrm)\\\\{([^{}]*)\\\\}\\\", r\\\"\\\\1\\\", str(answer))\\n+ answer = answer.replace(\\\"$\\\", \\\"\\\").strip()\\n+ return bool(re.fullmatch(r\\\"[+]?\\\\d{1,3}\\\", answer)) and 0 <= int(answer) <= 999\\n+\\n+ sources = train_dataset[\\\"source\\\"]\\n+ answers = train_dataset[\\\"Answer\\\"]\\n+ frontier_indices = [\\n+ i\\n+ for i, (source, answer) in enumerate(zip(sources, answers))\\n+ if source in {\\\"olympiads\\\", \\\"aops_forum\\\"} and is_aime_style_answer(answer)\\n+ ]\\n+ train_dataset = train_dataset.select(frontier_indices)\\n+ print(f\\\"Verified frontier curriculum: selected {len(train_dataset)} / {len(dataset['train'])} examples\\\")\\n*** Update File: /workspace/repo/opsd_trainer.py\\n@@\\n import os\\n import random\\n+import re\\n@@\\n required_columns = [\\n \\\"problem\\\",\\n \\\"solution\\\",\\n+ \\\"Answer\\\",\\n@@\\n def generalized_jsd_loss(\\n@@\\n token_clip=None,\\n+ sampled_token_ids=None,\\n+ outcome_correct=None,\\n+ outcome_weight=0.0,\\n+ position_scale=256.0,\\n@@\\n- # Masking\\n- if labels is not None:\\n- mask = labels != -100\\n- jsd = jsd[mask]\\n+ # Sum vocabulary contributions into a token loss. We retain the paper's\\n+ # pointwise vocabulary clipping, but discount late continuation tokens:\\n+ # after a long shared prefix the teacher nearly copies the student and the\\n+ # remaining tokens are much less likely to be a meaningful decision point.\\n+ token_jsd = jsd.sum(dim=-1)\\n+\\n+ if labels is not None:\\n+ mask = labels != -100\\n+ else:\\n+ mask = torch.ones_like(token_jsd, dtype=torch.bool)\\n+\\n+ positions = torch.arange(token_jsd.shape[1], device=token_jsd.device, dtype=token_jsd.dtype)\\n+ position_weights = 1.0 / (1.0 + positions / position_scale)\\n+ weights = mask.to(token_jsd.dtype) * position_weights.unsqueeze(0)\\n@@\\n if reduction == \\\"batchmean\\\":\\n- return jsd.sum() / mask.sum() if labels is not None else jsd.sum() / jsd.size(0)\\n+ loss = (token_jsd * weights).sum() / weights.sum().clamp_min(1.0)\\n elif reduction == \\\"sum\\\":\\n- return jsd.sum()\\n+ loss = (token_jsd * weights).sum()\\n elif reduction == \\\"mean\\\":\\n- return jsd.mean()\\n+ loss = token_jsd[mask].mean()\\n else:\\n- return jsd\\n+ return token_jsd\\n+\\n+ # Verified on-policy self-training complements the dense KL signal. It\\n+ # reinforces only trajectories that actually reach the reference answer;\\n+ # incorrect trajectories still receive dense OPSD feedback instead of being\\n+ # discarded as in pure rejection sampling/STaR.\\n+ if outcome_weight > 0 and sampled_token_ids is not None and outcome_correct is not None:\\n+ correct_mask = mask & outcome_correct.to(torch.bool).unsqueeze(1)\\n+ if correct_mask.any():\\n+ sampled_log_probs = torch.gather(\\n+ student_log_probs, dim=-1, index=sampled_token_ids.unsqueeze(-1)\\n+ ).squeeze(-1)\\n+ correct_weights = correct_mask.to(token_jsd.dtype) * position_weights.unsqueeze(0)\\n+ rejection_sft = -(sampled_log_probs * correct_weights).sum() / correct_weights.sum().clamp_min(1.0)\\n+ loss = loss + outcome_weight * rejection_sft\\n+\\n+ return loss\\n+\\n+ @staticmethod\\n+ def _extract_boxed_answer(text):\\n+ \\\"\\\"\\\"Extract the final balanced \\\\\\\\boxed{...} expression.\\\"\\\"\\\"\\n+ idx = text.rfind(\\\"\\\\\\\\boxed\\\")\\n+ if idx < 0:\\n+ return None\\n+ left = text.find(\\\"{\\\", idx)\\n+ if left < 0:\\n+ return None\\n+ depth = 0\\n+ for pos in range(left, len(text)):\\n+ if text[pos] == \\\"{\\\":\\n+ depth += 1\\n+ elif text[pos] == \\\"}\\\":\\n+ depth -= 1\\n+ if depth == 0:\\n+ return text[left + 1 : pos]\\n+ return None\\n+\\n+ @staticmethod\\n+ def _normalize_direct_answer(answer):\\n+ if answer is None:\\n+ return None\\n+ answer = re.sub(r\\\"\\\\\\\\(?:text|mathrm)\\\\{([^{}]*)\\\\}\\\", r\\\"\\\\1\\\", str(answer))\\n+ answer = answer.replace(\\\"$\\\", \\\"\\\").replace(\\\"\\\\\\\\,\\\", \\\"\\\").replace(\\\",\\\", \\\"\\\")\\n+ answer = re.sub(r\\\"\\\\s+\\\", \\\"\\\", answer).lower()\\n+ if re.fullmatch(r\\\"[+]?\\\\d+\\\", answer):\\n+ return str(int(answer))\\n+ return answer\\n@@\\n loss = self.generalized_jsd_loss(\\n@@\\n token_clip=self.jsd_token_clip,\\n+ sampled_token_ids=sampled_token_ids,\\n+ outcome_correct=inputs.get(\\\"outcome_correct\\\"),\\n+ outcome_weight=self.lmbda,\\n@@\\n inputs[\\\"labels\\\"] = labels\\n+\\n+ # Grade the sampled on-policy response before the forward pass. The\\n+ # curriculum uses direct numeric answers, making exact normalization both\\n+ # fast and reliable (no symbolic grader or external verifier is involved).\\n+ outcome_correct = [\\n+ self._normalize_direct_answer(self._extract_boxed_answer(completion))\\n+ == self._normalize_direct_answer(answer)\\n+ for completion, answer in zip(completion_texts, inputs[\\\"answer_texts\\\"])\\n+ ]\\n+ inputs[\\\"outcome_correct\\\"] = torch.tensor(outcome_correct, dtype=torch.bool, device=generated_ids.device)\\n+ self._metrics[\\\"train\\\"][\\\"rollout_accuracy\\\"].append(sum(outcome_correct) / max(1, len(outcome_correct)))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));"
}exec result
Script completed
Wall time 0.0 seconds
Output:
{}#27
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolexec
args
{
"input": "const patch = \"*** Begin Patch\\n*** Add File: /workspace/submission/recipe.env\\n+lmbda=0.01\\n+lr_scheduler_type=linear\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\nconst r = await tools.exec_command({cmd:\"python -m py_compile opsd_train.py opsd_trainer.py data_collator.py && python - <<'PY'\\nimport torch\\nfrom opsd_trainer import OPSDTrainer\\ns=torch.randn(2,5,17,requires_grad=True)\\nt=torch.randn(2,5,17)\\nlabels=torch.tensor([[1,2,3,-100,-100],[1,2,3,4,5]])\\nids=torch.randint(0,17,(2,5))\\nloss=OPSDTrainer.generalized_jsd_loss(s,t,labels,beta=0,temperature=1.1,token_clip=.05,sampled_token_ids=ids,outcome_correct=torch.tensor([1,0]),outcome_weight=.01)\\nprint('loss',loss.item())\\nloss.backward();print('grad',s.grad.norm().item())\\nfor x in ['answer \\\\\\\\boxed{025}.','\\\\\\\\boxed{\\\\\\\\text{123}}','no']:\\n print(x,OPSDTrainer._normalize_direct_answer(OPSDTrainer._extract_boxed_answer(x)))\\nPY\\ngit diff --stat && git diff --check && cat /workspace/.timer/remaining_secs\",\"workdir\":\"/workspace/repo\",\"yield_time_ms\":10000,\"max_output_tokens\":12000});\ntext(r.output);"
}exec result
Script completed
Wall time 10.2 seconds
Output:
{}#28
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolexec
args
{
"input": "const r = await tools.exec_command({cmd:\"python -m py_compile opsd_train.py opsd_trainer.py data_collator.py; git diff --check; git diff --stat; cat /workspace/submission/recipe.env; cat /workspace/.timer/remaining_secs\",\"workdir\":\"/workspace/repo\",\"yield_time_ms\":10000,\"max_output_tokens\":5000});\ntext(JSON.stringify(r));"
}exec result
Script completed
Wall time 0.2 seconds
Output:
{"chunk_id":"f4ef4d","wall_time_seconds":0.00000461,"exit_code":0,"original_token_count":105,"output":"README.md:285: trailing whitespace.\n+| `--jsd_token_clip` | 0.05 | Clip the JSD loss for each token to a maximum value. This can improve stability. | \n README.md | 4 +--\n data_collator.py | 4 +++\n opsd_train.py | 21 ++++++++++++++\n opsd_trainer.py | 88 ++++++++++++++++++++++++++++++++++++++++++++++++++++----\n 4 files changed, 108 insertions(+), 9 deletions(-)\nlmbda=0.01\nlr_scheduler_type=linear\n21270\n"}#29
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolexec
args
{
"input": "const p = await tools.update_plan({explanation:\"Implemented a first candidate combining frontier data selection, early-token-weighted dense OPSD, and verified rejection-SFT on correct on-policy rollouts.\",plan:[\n {step:\"Inspect baseline code, paper, logs, and available training controls\",status:\"completed\"},\n {step:\"Formulate and implement an OPSD method improvement\",status:\"completed\"},\n {step:\"Run capped training and diagnostic evaluation\",status:\"in_progress\"},\n {step:\"Refine candidate and repeat full evaluations\",status:\"pending\"},\n {step:\"Verify final code and recipe match official constraints\",status:\"pending\"}\n]});\nconst r = await tools.exec_command({cmd:\"SEED=42 OUT_DIR=/workspace/runs/frontier_v1 PORT=12950 bash /workspace/train_capped.sh\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":12000,\"tty\":true});\ntext(p);\ntext(JSON.stringify(r));"
}exec result
Script completed
Wall time 1.2 seconds
Output:
{}{"chunk_id":"a3e0a8","wall_time_seconds":1.00204879,"session_id":35523,"original_token_count":0,"output":""}#30
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolexec
args
{
"input": "const r = await tools.write_stdin({session_id:35523,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(r.output);"
}exec result
Script completed
Wall time 5.0 seconds
Output:
df: /root/.triton/autotune: No such file or directory
#31
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolexec
args
{
"input": "const r = await tools.write_stdin({session_id:35523,chars:\"\",yield_time_ms:30000,max_output_tokens:16000});\ntext(r.output);"
}exec result
Script running with cell ID 28
Wall time 11.0 seconds
Output:
#32
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolwait
args
{
"cell_id": "28",
"yield_time_ms": 30000,
"max_tokens": 16000
}wait result
Script completed
Wall time 11.0 seconds
Output:
<string>:24: FutureWarning: `torch_dtype` is deprecated and will be removed in version 0.27.0, please use `dtype` instead.
================================================================================
RUN CONFIGURATION
================================================================================
WandB Run Name: capped_seed42_lr5e-6_bs32
Output Directory: /workspace/runs/frontier_v1/capped_seed42
================================================================================
================================================================================
Loading model with dtype: torch.bfloat16
Using attention implementation: flash_attention_2
================================================================================
<string>:24: FutureWarning: `torch_dtype` is deprecated and will be removed in version 0.27.0, please use `dtype` instead.
================================================================================
RUN CONFIGURATION
================================================================================
WandB Run Name: capped_seed42_lr5e-6_bs32
Output Directory: /workspace/runs/frontier_v1/capped_seed42
================================================================================
================================================================================
Loading model with dtype: torch.bfloat16
Using attention implementation: flash_attention_2
================================================================================
<string>:24: FutureWarning: `torch_dtype` is deprecated and will be removed in version 0.27.0, please use `dtype` instead.
================================================================================
RUN CONFIGURATION
================================================================================
WandB Run Name: capped_seed42_lr5e-6_bs32
Output Directory: /workspace/runs/frontier_v1/capped_seed42
================================================================================
================================================================================
Loading model with dtype: torch.bfloat16
Using attention implementation: flash_attention_2
================================================================================
<string>:24: FutureWarning: `torch_dtype` is deprecated and will be removed in version 0.27.0, please use `dtype` instead.
================================================================================
RUN CONFIGURATION
================================================================================
WandB Run Name: capped_seed42_lr5e-6_bs32
Output Directory: /workspace/runs/frontier_v1/capped_seed42
================================================================================
================================================================================
Loading model with dtype: torch.bfloat16
Using attention implementation: flash_attention_2
================================================================================
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 Mon Aug 3 04:42:17 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 Mon Aug 3 04:42:17 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 Mon Aug 3 04:42:17 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 Mon Aug 3 04:42:17 2026).
Verified frontier curriculum: selected 8822 / 29434 examples
[DataCollator] Original padding_side: left
[DataCollator] Set padding_side to: right
[DataCollator] Reason first mode: False
Verified frontier curriculum: selected 8822 / 29434 examples
[DataCollator] Original padding_side: left
[DataCollator] Set padding_side to: right
[DataCollator] Reason first mode: False
`torch_dtype` is deprecated! Use `dtype` instead!
`torch_dtype` is deprecated! Use `dtype` instead!
Verified frontier curriculum: selected 8822 / 29434 examples
[DataCollator] Original padding_side: left
[DataCollator] Set padding_side to: right
[DataCollator] Reason first mode: False
Verified frontier curriculum: selected 8822 / 29434 examples
[DataCollator] Original padding_side: left
[DataCollator] Set padding_side to: right
[DataCollator] Reason first mode: False
`torch_dtype` is deprecated! Use `dtype` instead!
Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]`torch_dtype` is deprecated! Use `dtype` instead!
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Converting train dataset to ChatML: 100%|██████████| 8822/8822 [00:01<00:00, 4804.40 examples/s]
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reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolexec
args
{
"input": "const r = await tools.write_stdin({session_id:35523,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);"
}exec result
Script running with cell ID 29
Wall time 11.0 seconds
Output:
#34
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolwait
args
{
"cell_id": "29",
"yield_time_ms": 30000,
"max_tokens": 5000
}wait result
Script completed
Wall time 16.2 seconds
Output:
Warning: truncated output (original token count: 5027)
Total output lines: 4
Warning: truncated output (original token count: 7095)
Total output lines: 1
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Tokenizing train dataset: 79%|███████▉ | 7000/8822 [00:46<00:20, 89.26 examples/s]
Tokenizing train dataset: 80%|███████▉ | 7022/8822 [00:46<00:18, 99.09 examples/s]
Tokenizing train dataset: 80%|███████▉ | 7036/8822 [00:46<00:17, 103.86 examples/s]
Tokenizing train dataset: 80%|███████▉ | 7052/8822 [00:46<00:15, 111.93 examples/s]
Tokenizing train dataset: 80%|████████ | 7073/8822 [00:47<00:14, 117.34 examples/s]
Tokenizing train dataset: 80%|████████ | 7088/8822 [00:47<00:14, 121.54 examples/s]
Tokenizing train dataset: 81%|████████ | 7105/8822 [00:47<00:13, 131.43 examples/s]
Tokenizing train dataset: 81%|████████ | 7121/8822 [00:47<00:12, 134.57 examples/s]
Tokenizing train dataset: 81%|████████ | 7139/8822 [00:47<00:11, 142.30 examples/s]
Tokenizing train dataset: 81%|████████ | 7159/8822 [00:47<00:10, 153.14 examples/s]
Tokenizing train dataset: 81%|████████▏ | 7181/8822 [00:47<00:11, 145.90 examples/s]
Tokenizing train dataset: 82%|████████▏ | 7199/8822 [00:47<00:10, 150.59 examples/s]
Tokenizing train dataset: 82%|████████▏ | 7218/8822 [00:47<00:10, 154.42 examples/s]
Tokenizing train dataset: 82%|████████▏ | 7236/8822 [00:48<00:10, 156.99 examples/s]
Tokenizing train dataset: 82%|████████▏ | 7253/8822 [00:48<00:09, 157.21 examples/s]
Tokenizing train dataset: 82%|████████▏ | 7270/8822 [00:48<00:09, 157.10 examples/s]
Tokenizing train dataset: 83%|████████▎ | 7286/8822 [00:48<00:10, 152.70 examples/s]
Tokenizing train dataset: 83%|████████▎ | 7304/8822 [00:48<00:09, 155.49 examples/s]
Tokenizing train dataset: 83%|████████▎ | 7326/8822 [00:48<00:10, 148.64 examples/s]
Tokenizing train dataset: 83%|████████▎ | 7343/8822 [00:48<00:09, 148.28 examples/s]
Tokenizing train dataset: 83%|████████▎ | 7359/8822 [00:48<00:09, 147.41 examples/s]
Tokenizing train dataset: 84%|████████▎ | 7377/8822 [00:49<00:09, 153.41 examples/s]
Tokenizing train dataset: 84%|████████▍ | 7397/8822 [00:49<00:08, 163.10 examples/s]
Tokenizing train dataset: 84%|████████▍ | 7414/8822 [00:49<00:08, 159.78 examples/s]
Tokenizing train dataset: 84%|████████▍ | 7432/8822 [00:49<00:08, 161.86 examples/s]
Tokenizing train dataset: 84%|████████▍ | 7454/8822 [00:49<00:07, 175.55 examples/s]
Tokenizing train dataset: 85%|████████▍ | 7477/8822 [00:49<00:08, 165.18 examples/s]
Tokenizing train dataset: 85%|████████▍ | 7497/8822 [00:49<00:07, 170.77 examples/s]
Tokenizing train dataset: 85%|████████▌ | 7523/8822 [00:49<00:07, 169.44 examples/s]
Tokenizing train dataset: 85%|████████▌ | 7542/8822 [00:49<00:07, 172.48 examples/s]
Tokenizing train dataset: 86%|████████▌ | 7566/8822 [00:50<00:07, 166.71 examples/s]
Tokenizing train dataset: 86%|████████▌ | 7585/8822 [00:50<00:07, 170.27 examples/s]
Tokenizing train dataset: 86%|████████▌ | 7603/8822 [00:50<00:07, 168.35 examples/s]
Tokenizing train dataset: 86%|████████▋ | 7620/8822 [00:50<00:07, 166.22 examples/s]
Tokenizing train dataset: 87%|████████▋ | 7642/8822 [00:50<00:07, 153.14 examples/s]
Tokenizing train dataset: 87%|████████▋ | 7658/8822 [00:50<00:07, 150.33 examples/s]
Tokenizing train dataset: 87%|████████▋ | 7681/8822 [00:50<00:07, 146.61 examples/s]
Tokenizing train dataset: 87%|████████▋ | 7696/8822 [00:51<00:07, 144.70 examples/s]
Tokenizing train dataset: 87%|████████▋ | 7712/8822 [00:51<00:07, 146.34 examples/s]
Tokenizing train dataset: 88%|████████▊ | 7729/8822 [00:51<00:07, 148.99 examples/s]
Tokenizing train dataset: 88%|████████▊ | 7751/8822 [00:51<00:07, 146.45 examples/s]
Tokenizing train dataset: 88%|████████▊ | 7768/8822 [00:51<00:07, 148.98 examples/s]
Tokenizing train dataset: 88%|████████▊ | 7789/8822 [00:51<00:07, 143.65 examples/s]
Tokenizing train dataset: 88%|████████▊ | 7805/8822 [00:51<00:06, 146.17 examples/s]
Tokenizing train dataset: 89%|████████▊ | 7826/8822 [00:51<00:07, 141.42 examples/s]
Tokenizing train dataset: 89%|████████▉ | 7842/8822 [00:52<00:06, 141.44 examples/s]
Tokenizing train dataset: 89%|████████▉ | 7860/8822 [00:52<00:06, 145.42 examples/s]
Tokenizing train dataset: 89%|████████▉ | 7875/8822 [00:52<00:06, 143.37 examples/s]
Tokenizing train dataset: 90%|████████▉ | 7897/8822 [00:52<00:06, 143.45 examples/s]
Tokenizing train dataset: 90%|████████▉ | 7914/8822 [00:52<00:06, 145.21 examples/s]
Tokenizing train dataset: 90%|████████▉ | 7929/8822 [00:52<00:06, 141.96 examples/s]
Tokenizing train dataset: 90%|█████████ | 7944/8822 [00:52<00:06, 142.38 examples/s]
Tokenizing train dataset: 90%|█████████ | 7960/8822 [00:52<00:05, 144.89 examples/s]
Tokenizing train dataset: 90%|█████████ | 7976/8822 [00:52<00:05, 143.85 examples/s]
Tokenizing train dataset: 91%|█████████ | 7993/8822 [00:53<00:05, 149.40 examples/s]
Tokenizing train dataset: 91%|█████████ | 8012/8822 [00:53<00:16, 49.58 examples/s]
Tokenizing train dataset: 91%|█████████ | 8026/8822 [00:54<00:13, 59.27 examples/s]
Tokenizing train dataset: 91%|█████████ | 8042/8822 [00:54<00:10, 71.85 examples/s]
Tokenizing train dataset: 91%|█████████▏| 8057/8822 [00:54<00:09, 83.87 examples/s]
Tokenizing train dataset: 91%|█████████▏| 8071/8822 [00:54<00:08, 91.95 examples/s]
Tokenizing train dataset: 92%|█████████▏| 8088/8822 [00:54<00:06, 106.12 examples/s]
Tokenizing train dataset: 92%|█████████▏| 8105/8822 [00:54<00:06, 117.09 examples/s]#35
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolexec
args
{
"input": "const r = await tools.write_stdin({session_id:35523,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);"
}exec result
Script running with cell ID 30
Wall time 11.0 seconds
Output:
#36
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolwait
args
{
"cell_id": "30",
"yield_time_ms": 30000,
"max_tokens": 5000
}wait result
Script completed
Wall time 16.3 seconds
Output:
Tokenizing train dataset: 92%|█████████▏| 8121/8822 [00:54<00:05, 122.68 examples/s]
Tokenizing train dataset: 92%|█████████▏| 8136/8822 [00:54<00:05, 127.26 examples/s]
Tokenizing train dataset: 92%|█████████▏| 8152/8822 [00:54<00:05, 132.63 examples/s]
Tokenizing train dataset: 93%|█████████▎| 8167/8822 [00:55<00:04, 132.61 examples/s]
Tokenizing train dataset: 93%|█████████▎| 8183/8822 [00:55<00:04, 136.77 examples/s]
Tokenizing train dataset: 93%|█████████▎| 8205/8822 [00:55<00:04, 137.16 examples/s]
Tokenizing train dataset: 93%|█████████▎| 8221/8822 [00:55<00:04, 138.93 examples/s]
Tokenizing train dataset: 93%|█████████▎| 8237/8822 [00:55<00:04, 141.35 examples/s]
Tokenizing train dataset: 94%|█████████▎| 8253/8822 [00:55<00:04, 142.22 examples/s]
Tokenizing train dataset: 94%|█████████▎| 8269/8822 [00:55<00:03, 143.60 examples/s]
Tokenizing train dataset: 94%|█████████▍| 8285/8822 [00:55<00:03, 145.96 examples/s]
Tokenizing train dataset: 94%|█████████▍| 8305/8822 [00:56<00:03, 138.86 examples/s]
Tokenizing train dataset: 94%|█████████▍| 8322/8822 [00:56<00:03, 143.34 examples/s]
Tokenizing train dataset: 95%|█████████▍| 8338/8822 [00:56<00:03, 146.34 examples/s]
Tokenizing train dataset: 95%|█████████▍| 8353/8822 [00:56<00:03, 145.90 examples/s]
Tokenizing train dataset: 95%|█████████▍| 8371/8822 [00:56<00:03, 149.19 examples/s]
Tokenizing train dataset: 95%|█████████▌| 8388/8822 [00:56<00:02, 150.26 examples/s]
Tokenizing train dataset: 95%|█████████▌| 8405/8822 [00:56<00:02, 153.73 examples/s]
Tokenizing train dataset: 95%|█████████▌| 8422/8822 [00:56<00:02, 156.25 examples/s]
Tokenizing train dataset: 96%|█████████▌| 8441/8822 [00:56<00:02, 161.89 examples/s]
Tokenizing train dataset: 96%|█████████▌| 8466/8822 [00:57<00:02, 160.71 examples/s]
Tokenizing train dataset: 96%|█████████▌| 8489/8822 [00:57<00:02, 155.54 examples/s]
Tokenizing train dataset: 96%|█████████▋| 8505/8822 [00:57<00:02, 155.41 examples/s]
Tokenizing train dataset: 97%|█████████▋| 8522/8822 [00:57<00:01, 157.54 examples/s]
Tokenizing train dataset: 97%|█████████▋| 8540/8822 [00:57<00:01, 158.46 examples/s]
Tokenizing train dataset: 97%|█████████▋| 8559/8822 [00:57<00:01, 163.33 examples/s]
Tokenizing train dataset: 97%|█████████▋| 8583/8822 [00:57<00:01, 157.41 examples/s]
Tokenizing train dataset: 97%|█████████▋| 8601/8822 [00:57<00:01, 159.74 examples/s]
Tokenizing train dataset: 98%|█████████▊| 8625/8822 [00:58<00:01, 156.07 examples/s]
Tokenizing train dataset: 98%|█████████▊| 8647/8822 [00:58<00:01, 147.79 examples/s]
Tokenizing train dataset: 98%|█████████▊| 8663/8822 [00:58<00:01, 149.85 examples/s]
Tokenizing train dataset: 98%|█████████▊| 8681/8822 [00:58<00:00, 155.15 examples/s]
Tokenizing train dataset: 99%|█████████▊| 8698/8822 [00:58<00:00, 157.30 examples/s]
Tokenizing train dataset: 99%|█████████▉| 8714/8822 [00:58<00:00, 153.73 examples/s]
Tokenizing train dataset: 99%|█████████▉| 8736/8822 [00:58<00:00, 147.17 examples/s]
Tokenizing train dataset: 99%|█████████▉| 8751/8822 [00:58<00:00, 144.60 examples/s]
Tokenizing train dataset: 99%|█████████▉| 8768/8822 [00:59<00:00, 147.14 examples/s]
Tokenizing train dataset: 100%|█████████▉| 8786/8822 [00:59<00:00, 153.22 examples/s]
Tokenizing train dataset: 100%|█████████▉| 8808/8822 [00:59<00:00, 147.62 examples/s]
Tokenizing train dataset: 100%|██████████| 8822/8822 [00:59<00:00, 147.65 examples/s]
Truncating train dataset: 0%| | 0/8822 [00:00<?, ? examples/s]
Truncating train dataset: 45%|████▌ | 4000/8822 [00:00<00:00, 22942.17 examples/s]
Truncating train dataset: 91%|█████████ | 8000/8822 [00:00<00:00, 22823.71 examples/s]
Truncating train dataset: 100%|██████████| 8822/8822 [00:00<00:00, 22252.21 examples/s]
Detected kernel version 4.19.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
================================================================================
FIXED TEACHER MODE ENABLED
Teacher will use the initial policy (base model without LoRA adapters)
Student will update with LoRA adapters
================================================================================
================================================================================
FIXED TEACHER MODE ENABLED
Teacher will use the initial policy (base model without LoRA adapters)
Student will update with LoRA adapters
================================================================================
================================================================================
FIXED TEACHER MODE ENABLED
Teacher will use the initial policy (base model without LoRA adapters)
Student will update with LoRA adapters
================================================================================
================================================================================
FIXED TEACHER MODE ENABLED
Teacher will use the initial policy (base model without LoRA adapters)
Student will update with LoRA adapters
================================================================================
`torch_dtype` is deprecated! Use `dtype` instead!
`torch_dtype` is deprecated! Use `dtype` instead!
`torch_dtype` is deprecated! Use `dtype` instead!
`torch_dtype` is deprecated! Use `dtype` instead!
[Gloo] Rank [Gloo] Rank 10 is connected to 3 peer ranks. Expected number of connected peer ranks is : 3
is connected to 3 peer ranks. [Gloo] Rank Expected number of connected peer ranks is : 2 is connected to 33 peer ranks. Expected number of connected peer ranks is :
3
[Gloo] Rank 3 is connected to 3 peer ranks. Expected number of connected peer ranks is : 3
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
Loading safetensors checkpoint shards: 0% Completed | 0/2 [00:00<?, ?it/s]
Loading safetensors checkpoint shards: 100% Completed | 2/2 [00:00<00:00, 2.14it/s]
Loading safetensors checkpoint shards: 100% Completed | 2/2 [00:00<00:00, 2.14it/s]
#37
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolexec
args
{
"input": "const r = await tools.write_stdin({session_id:35523,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);"
}exec result
Script running with cell ID 31
Wall time 11.0 seconds
Output:
#38
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolwait
args
{
"cell_id": "31",
"yield_time_ms": 30000,
"max_tokens": 5000
}wait result
Script completed
Wall time 3.4 seconds
Output:
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 0%| | 0/5 [00:00<?, ?it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 60%|██████ | 3/5 [00:00<00:00, 20.63it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 100%|██████████| 5/5 [00:00<00:00, 20.92it/s]
Capturing CUDA graphs (decode, FULL): 0%| | 0/4 [00:00<?, ?it/s]
Capturing CUDA graphs (decode, FULL): 100%|██████████| 4/4 [00:00<00:00, 35.83it/s]
Capturing CUDA graphs (decode, FULL): 100%|██████████| 4/4 [00:00<00:00, 35.71it/s]
The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'bos_token_id': None, 'pad_token_id': 151643}.
The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'bos_token_id': None, 'pad_token_id': 151643}.
The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'bos_token_id': None, 'pad_token_id': 151643}.
The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'bos_token_id': None, 'pad_token_id': 151643}.
[rank2]:W0803 04:46:05.875000 1165 torch/utils/cpp_extension.py:2425] TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation.
[rank2]:W0803 04:46:05.875000 1165 torch/utils/cpp_extension.py:2425] If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'] to specific architectures.
#39
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolexec
args
{
"input": "const r = await tools.write_stdin({session_id:35523,chars:\"\",yield_time_ms:30000,max_output_tokens:8000});\ntext(r.output);"
}exec result
Script running with cell ID 32
Wall time 11.0 seconds
Output:
#40
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolwait
args
{
"cell_id": "32",
"yield_time_ms": 30000,
"max_tokens": 8000
}wait result
Script completed
Wall time 10.5 seconds
Output:
0%| | 0/100 [00:00<?, ?it/s]vLLM generation done - elapsed time: 3.72s, prompts: 4, total tokens: 2785, avg length: 696.2, speed: 749.6 tok/s
vLLM generation done - elapsed time: 3.75s, prompts: 4, total tokens: 3158, avg length: 789.5, speed: 842.7 tok/s
vLLM generation done - elapsed time: 3.77s, prompts: 4, total tokens: 3511, avg length: 877.8, speed: 932.5 tok/s
vLLM generation done - elapsed time: 3.77s, prompts: 4, total tokens: 3617, avg length: 904.2, speed: 960.1 tok/s
/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:85: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
warnings.warn(
/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:85: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
warnings.warn(
/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:85: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
warnings.warn(
/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:85: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
warnings.warn(
Could not estimate the number of tokens of the input, floating-point operations will not be computed
Could not estimate the number of tokens of the input, floating-point operations will not be computed
Could not estimate the number of tokens of the input, floating-point operations will not be computed
Could not estimate the number of tokens of the input, floating-point operations will not be computed
vLLM generation done - elapsed time: 2.59s, prompts: 4, total tokens: 2432, avg length: 608.0, speed: 937.6 tok/s
vLLM generation done - elapsed time: 3.71s, prompts: 4, total tokens: 3317, avg length: 829.2, speed: 894.9 tok/s
vLLM generation done - elapsed time: 3.73s, prompts: 4, total tokens: 3522, avg length: 880.5, speed: 943.4 tok/s
vLLM generation done - elapsed time: 3.77s, prompts: 4, total tokens: 4096, avg length: 1024.0, speed: 1087.5 tok/s
1%| | 1/100 [00:09<15:07, 9.17s/it]vLLM generation done - elapsed time: 1.81s, prompts: 4, total tokens: 1679, avg length: 419.8, speed: 928.3 tok/s
vLLM generation done - elapsed time: 3.58s, prompts: 4, total tokens: 2547, avg length: 636.8, speed: 710.7 tok/s
vLLM generation done - elapsed time: 3.68s, prompts: 4, total tokens: 2961, avg length: 740.2, speed: 805.6 tok/s
vLLM generation done - elapsed time: 3.74s, prompts: 4, total tokens: 3749, avg length: 937.2, speed: 1001.7 tok/s
#41
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolexec
args
{
"input": "const r = await tools.write_stdin({session_id:35523,chars:\"\",yield_time_ms:30000,max_output_tokens:7000});\ntext(r.output);"
}exec result
Script running with cell ID 33
Wall time 11.0 seconds
Output:
#42
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolwait
args
{
"cell_id": "33",
"yield_time_ms": 30000,
"max_tokens": 7000
}wait result
Script completed
Wall time 16.1 seconds
Output:
vLLM generation done - elapsed time: 3.57s, prompts: 4, total tokens: 2596, avg length: 649.0, speed: 726.2 tok/s
vLLM generation done - elapsed time: 3.66s, prompts: 4, total tokens: 2848, avg length: 712.0, speed: 778.3 tok/s
vLLM generation done - elapsed time: 3.73s, prompts: 4, total tokens: 3482, avg length: 870.5, speed: 934.1 tok/s
vLLM generation done - elapsed time: 3.73s, prompts: 4, total tokens: 3968, avg length: 992.0, speed: 1062.9 tok/s
2%|▏ | 2/100 [00:18<14:40, 8.98s/it]
{'loss': 0.0179, 'grad_norm': 0.18278871476650238, 'learning_rate': 4.95e-06, 'on_policy_loss': 0.0179, 'rollout_accuracy': 0.4375, 'epoch': 0.01}
2%|▏ | 2/100 [00:18<14:40, 8.98s/it]vLLM generation done - elapsed time: 2.50s, prompts: 4, total tokens: 1481, avg length: 370.2, speed: 592.5 tok/s
vLLM generation done - elapsed time: 3.63s, prompts: 4, total tokens: 2599, avg length: 649.8, speed: 716.2 tok/s
vLLM generation done - elapsed time: 3.69s, prompts: 4, total tokens: 3041, avg length: 760.2, speed: 823.8 tok/s
vLLM generation done - elapsed time: 3.69s, prompts: 4, total tokens: 3246, avg length: 811.5, speed: 879.4 tok/s
vLLM generation done - elapsed time: 3.61s, prompts: 4, total tokens: 2620, avg length: 655.0, speed: 726.1 tok/s
vLLM generation done - elapsed time: 3.68s, prompts: 4, total tokens: 3401, avg length: 850.2, speed: 923.1 tok/s
vLLM generation done - elapsed time: 3.69s, prompts: 4, total tokens: 3067, avg length: 766.8, speed: 831.5 tok/s
vLLM generation done - elapsed time: 3.73s, prompts: 4, total tokens: 3693, avg length: 923.2, speed: 990.3 tok/s
3%|▎ | 3/100 [00:26<14:26, 8.93s/it]vLLM generation done - elapsed time: 3.56s, prompts: 4, total tokens: 2573, avg length: 643.2, speed: 723.6 tok/s
vLLM generation done - elapsed time: 3.64s, prompts: 4, total tokens: 2856, avg length: 714.0, speed: 783.9 tok/s
vLLM generation done - elapsed time: 3.74s, prompts: 4, total tokens: 3674, avg length: 918.5, speed: 983.3 tok/s
vLLM generation done - elapsed time: 3.74s, prompts: 4, total tokens: 3559, avg length: 889.8, speed: 950.5 tok/s
vLLM generation done - elapsed time: 3.68s, prompts: 4, total tokens: 3230, avg length: 807.5, speed: 878.5 tok/s
vLLM generation done - elapsed time: 3.69s, prompts: 4, total tokens: 3446, avg length: 861.5, speed: 933.2 tok/s
vLLM generation done - elapsed time: 3.74s, prompts: 4, total tokens: 3652, avg length: 913.0, speed: 976.0 tok/s
vLLM generation done - elapsed time: 3.75s, prompts: 4, total tokens: 3650, avg length: 912.5, speed: 974.2 tok/s
4%|▍ | 4/100 [00:35<14:17, 8.94s/it]
{'loss': 0.0191, 'grad_norm': 0.20440655946731567, 'learning_rate': 4.85e-06, 'on_policy_loss': 0.0191, 'rollout_accuracy': 0.5, 'epoch': 0.01}
4%|▍ | 4/100 [00:36<14:17, 8.94s/it]vLLM generation done - elapsed time: 3.05s, prompts: 4, total tokens: 2109, avg length: 527.2, speed: 691.8 tok/s
vLLM generation done - elapsed time: 3.57s, prompts: 4, total tokens: 2411, avg length: 602.8, speed: 675.4 tok/s
vLLM generation done - elapsed time: 3.71s, prompts: 4, total tokens: 3387, avg length: 846.8, speed: 913.4 tok/s
vLLM generation done - elapsed time: 3.74s, prompts: 4, total tokens: 3781, avg length: 945.2, speed: 1011.2 tok/s
vLLM generation done - elapsed time: 3.63s, prompts: 4, total tokens: 2975, avg length: 743.8, speed: 819.6 tok/s
vLLM generation done - elapsed time: 3.69s, prompts: 4, total tokens: 3224, avg length: 806.0, speed: 873.8 tok/s
vLLM generation done - elapsed time: 3.71s, prompts: 4, total tokens: 3332, avg length: 833.0, speed: 897.8 tok/s
vLLM generation done - elapsed time: 3.71s, prompts: 4, total tokens: 3424, avg length: 856.0, speed: 922.5 tok/s
5%|▌ | 5/100 [00:44<14:04, 8.89s/it]vLLM generation done - elapsed time: 3.43s, prompts: 4, total tokens: 3220, avg length: 805.0, speed: 938.8 tok/s
vLLM generation done - elapsed time: 3.69s, prompts: 4, total tokens: 3366, avg length: 841.5, speed: 911.2 tok/s
vLLM generation done - elapsed time: 3.75s, prompts: 4, total tokens: 3995, avg length: 998.8, speed: 1065.3 tok/s
vLLM generation done - elapsed time: 3.77s, prompts: 4, total tokens: 4053, avg length: 1013.2, speed: 1076.0 tok/s
#43
narration
The first capped run is healthy. The selected training frontier has 8,822 examples; sampled rollouts are currently correct about 44–50% of the time, so both halves of the hybrid objective are active. Early gradient norms are around 0.18–0.20 (comparable to the baseline’s initial 0.17 and safely clipped at 0.1), with no memory or distributed-training regressions.
#44
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolexec
args
{
"input": "const r = await tools.write_stdin({session_id:35523,chars:\"\",yield_time_ms:50000,max_output_tokens:2500});\ntext(r.output);"
}exec result
Script running with cell ID 34
Wall time 11.0 seconds
Output:
#45
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolwait
args
{
"cell_id": "34",
"yield_time_ms": 50000,
"max_tokens": 2500
}wait result
Script completed
Wall time 35.6 seconds
Output:
Warning: truncated output (original token count: 2527)
Total output lines: 225
Warning: truncated output (original token count: 4138)
Total output lines: 327
vLLM generation done - elapsed time: 3.65s, prompts: 4, total tokens: 3141, avg length: 785.2, speed: 860.3 tok/s
vLLM generation done - elapsed time: 3.66s, prompts: 4, total tokens: 3112, avg length: 778.0, speed: 849.8 tok/s
vLLM generation done - elapsed time: 3.74s, prompts: 4, total tokens: 3685, avg length: 921.2, speed: 985.8 tok/s
vLLM generation done - elapsed time: 3.76s, prompts: 4, total tokens: 4054, avg length: 1013.5, speed: 1079.2 tok/s
================================================================================
Saved 48 generation outputs to:
/workspace/runs/frontier_v1/capped_seed42/generations/generations_step_5.json
================================================================================
6%|▌ | 6/100 [00:53<13:56, 8.90s/it]
{'loss': 0.0136, 'grad_norm': 0.1776663064956665, 'learning_rate': 4.75e-06, 'on_policy_loss': 0.0136, 'rollout_accuracy': 0.25, 'epoch': 0.02}
6%|▌ | 6/100 [00:53<13:56, 8.90s/it]vLLM generation done - elapsed time: 3.64s, prompts: 4, total tokens: 3068, avg length: 767.0, speed: 842.0 tok/s
vLLM generation done - elapsed time: 3.73s, prompts: 4, total tokens: 3469, avg length: 867.2, speed: 929.3 tok/s
vLLM generation done - elapsed time: 3.73s, prompts: 4, total tokens: 3607, avg length: 901.8, speed: 966.0 tok/s
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vLLM generation done - elapsed time: 3.74s, prompts: 4, total tokens: 3576, avg length: 894.0, speed: 956.7 tok/s
================================================================================
STUDENT GENERATION SAMPLE (Step 6):
================================================================================
Prompt:
<|im_start|>user
Problem: In how many ways can we arrange 7 white balls and 5 black balls in a line so that there is at least one white ball between any two black balls?
Please reason step by step, and put your final answer within \boxed{}.<|im_end|>
<|im_start|>assistant
<think>
</think>
<|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|>
Completion:
We are given the problem:
> In how many ways can we arrange 7 white balls and 5 black balls in a line so that there is at least one white ball between any two black balls?
---
### Step 1: Understand the Constraints
We are to arrange **7 white balls (W)** and **5 black balls (B)** in a line.
The key constraint is that **there must be at least one white ball between any two black balls**.
This means no two black balls can be adjacent — that is, **no two B's can be next to each other**.
---
### Step 2: Model the Problem
We can think of this as placing **5 black balls** (B) in a line such that there's at least one white ball (W) between them.
To ensure that, we can model the problem using **combinatorics** with **stars and bars** or **gap method**.
Let's first place the **5 black balls**:
We need to place 5 black balls with **at least one white ball between them**. This implies that we need to place at least **4 white balls** between the black balls (since there are 4 gaps between 5 black balls).
That gives us:
- 5 black balls (B)
- 4 white balls (W) to place between them
- Total so far: 5 + 4 = 9
So we have:
- 9 items: 5 B's and 4 W's
- Remaining to place: $7 - 9 = -2$ → Wait, this is wrong.
Wait! We have **7 white balls** and **5 black balls**. Let's correct this.
---
### Step 3: Use the "Stars and Bars" Method
We want to place **5 black balls** and **7 white balls** in a line such that **no two black balls are adjacent**.
This is a classic **non-adjacent placement** problem.
---
### Step 4: Transform the Problem
Let’s model the arrangement as:
We place 5 black balls (B) with at least one white ball (W) between them.
That means we need at least **4 white balls** to separate the black balls.
Now distribute the remaining white balls.
We have:
- 5 black balls
- 7 white balls
We've already allocated **4 white balls** to separate the black balls → that uses up 4 Ws.
Now we have $7 - 4 = 3$ white…27 tokens truncated…secutive or differ by only 1**.
So, subsets that have **no two consecutive numbers** (and also must have at least two elements) will satisfy this.
This is similar to a **non-consecutive subset selection** problem, which is a classic combinatorics problem.
---
### Step 2: Count Subsets of $ S $ with No Two Consecutive Elements
We can count the number of subsets of $ S $ of size at least 2, where **no two elements are consecutive**.
We can use **combinatorics** to count such subsets.
Let’s denote the number of such subsets of size $ k $, with no two elements consecutive.
This is a standard result in combinatorics, and the number of such subsets of size $ k $ from a set of size $ n $ is:
$$
\binom{n - k + 1}{k}
$$
But we want all **subsets of size $ \geq 2 $**. So we need to sum over $ k = 2 $ to $ k = 10 $:
$$
\sum_{k=2}^{10} \binom{10 - k + 1}{k}
$$
But let’s **reformulate the problem** more easily.
---
### Step 3: Reformulate the Problem
We can consider the number of subsets of $ S $ of size $ \geq 2 $ with **no two consecutive numbers**.
Let’s **think in terms of placing elements in positions**.
We are choosing a subset of $ S = \{1, 2, 3, \ldots, 10\} $, and we require:
- The subset has **at least 2 elements**.
- No two elements are **consecutive**.
This is equivalent to counting the number of **non-consecutive subsets** of size $ \geq 2 $.
To count all such subsets, we can compute the total number of **all subsets** of $ S $ with at most 10 elements, and then subtract the subsets that are **non-consecutive** of size less than 2 (i.e., size 0 or 1).
Let’s denote:
- Total number of subsets of $ S $: $ 2^{10} = 1024 $
We need to count the number of **non-consecutive subsets** of **size ≥ 2**.
---
### Step 4: Count the Number of Non-Consecutive Subsets of Size 0, 1, and ≥2
We can count:
- Subsets of size 0: 1 (empty set)
- Subsets of size 1: 10 (each single element)
So, the number of subsets with **size ≥ 2** is:
$$
2^{10} - 1 - 10 = 1024 - 11 = 1013
$$
But not all of these subsets satisfy the **no two consecutive elements** condition.
---
### Step 5: Count the Number of Non-Consecutive Subsets of Size ≥2
We want to count the number of subsets of size $ \geq 2 $ such that **no two elements are consecutive**.
This is a known problem, and there is a standard formula.
The number of subsets of size $ k $ with no two consecutive elements from a set of size $ n $ is:
$$
\binom{n - k + 1}{k}
$$
So to count all subsets of size $ \geq 2 $ with no two consecutive elements:
$$
\sum_{k=2}^{10} \binom{10 - k + 1}{k} = \sum_{k=2}^{10} \binom{11 - k}{k}
$$
Let’s compute this sum manually.
We can list the values of $ \binom{11 - k}{k} $ for $ k = 2 $ to $ 10 $:
- $ k = 2 $: $ \binom{9}{2} = 36 $
================================================================================
================================================================================
Saved 40 generation outputs to:
/workspace/runs/frontier_v1/capped_seed42/generations/generations_step_10.json
================================================================================
11%|█ | 11/100 [01:38<13:13, 8.92s/it]vLLM generation done - elapsed time: 3.53s, prompts: 4, total tokens: 2570, avg length: 642.5, speed: 727.3 tok/s
vLLM generation done - elapsed time: 3.72s, prompts: 4, total tokens: 3449, avg length: 862.2, speed: 927.2 tok/s
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vLLM generation done - elapsed time: 3.54s, prompts: 4, total tokens: 2508, avg length: 627.0, speed: 708.3 tok/s
vLLM generation done - elapsed time: 3.68s, prompts: 4, total tokens: 3272, avg length: 818.0, speed: 889.0 tok/s
vLLM generation done - elapsed time: 3.69s, prompts: 4, total tokens: 3440, avg length: 860.0, speed: 931.0 tok/s
12%|█▏ | 12/100 [01:47<13:03, 8.91s/it]
{'loss': 0.0079, 'grad_norm': 0.11661431193351746, 'learning_rate': 4.450000000000001e-06, 'on_policy_loss': 0.0079, 'rollout_accuracy': 0.625, 'epoch': 0.04}
12%|█▏ | 12/100 [01:47<13:03, 8.91s/it]vLLM generation done - elapsed time: 3.61s, prompts: 4, total tokens: 2713, avg length: 678.2, speed: 751.3 tok/s
vLLM generation done - elapsed time: 3.67s, prompts: 4, total tokens: 3387, avg length: 846.8, speed: 922.0 tok/s
vLLM generation done - elapsed time: 3.75s, prompts: 4, total tokens: 4040, avg length: 1010.0, speed: 1078.5 tok/s
vLLM generation done - elapsed time: 3.76s, prompts: 4, total tokens: 3635, avg length: 908.8, speed: 967.3 tok/s
#46
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolexec
args
{
"input": "const r = await tools.write_stdin({session_id:35523,chars:\"\",yield_time_ms:50000,max_output_tokens:1000});\ntext(r.output);"
}exec result
Script running with cell ID 35
Wall time 11.0 seconds
Output:
#47
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolwait
args
{
"cell_id": "35",
"yield_time_ms": 50000,
"max_tokens": 1000
}wait result
Script completed
Wall time 35.3 seconds
Output:
Warning: truncated output (original token count: 1027)
Total output lines: 34
Warning: truncated output (original token count: 1840)
Total output lines: 61
vLLM generation done - elapsed time: 3.56s, prompts: 4, total tokens: 2502, avg length: 625.5, speed: 703.4 tok/s
vLLM generation done - elapsed time: 3.64s, prompts: 4, total tokens: 2685, avg length: 671.2, speed: 737.2 tok/s
vLLM generation done - elapsed time: 3.72s, prompts: 4, total tokens: 4095, avg length: 1023.8, speed: 1099.3 tok/s
vLLM generation done - elapsed time: 3.73s, prompts: 4, total tokens: 3371, avg length: 842.8, speed: 903.5 tok/s
13%|█▎ | 13/100 [01:56<12:53, 8.89s/it]vLLM generation done - elapsed time: 3.65s, prompts: 4, total tokens: 2689, avg length: 672.2, speed: 736.6 tok/s
vLLM generation done - elapsed time: 3.70s, prompts: 4, total tokens: 3371, avg length: 842.8, speed: 910.7 tok/s
vLLM generation done - elapsed time: 3.71s, prompts: 4, total tokens: 3277, avg length: 819.2, speed: 882.5 tok/s
vLLM generation done - elapsed time: 3.73s, prompts: 4, total tokens: 3614, avg length: 903.5, speed: 967.7 tok/s
vLLM generation done - elapsed time: 3.70s, prompts: 4, total tokens: 3626, avg length: 906.5, speed: 979.1 tok/s
vLLM generation done - elapsed time: 3.71s, prompts: 4, total tokens: 3614, avg length: 903.5, speed: 973.3 tok/s
vLLM generation done - elapsed time: 3.73s, prompts: 4, total tokens: 3856, avg length: 964.0, speed: 1032.4 tok/s
vLLM generation done - elapsed time: 3.74s, prompts: 4, total tokens: 3472, avg length: 868.0, speed: 928.7 tok/s
14%|█▍ | 14/100 [02:04<12:43, 8.88s/it]
{'loss': 0.0079, 'grad_norm': 0.09829960763454437, 'learning_rate': 4.350000000000001e-06, 'on_policy_loss': 0.0079, 'rollout_accuracy': 0.3125, 'epoch': 0.05}
14%|█▍ | 14/100 [02:05<12:43, 8.88s/it]vLLM generation done - elapsed time: 3.71s, prompts: 4, total tokens: 3611, avg length: 902.8, speed: 972.1 tok/s
vLLM generation done - elapsed time: 3.74s, prompts: …27 tokens truncated…ed time: 3.65s, prompts: 4, total tokens: 2866, avg length: 716.5, speed: 785.6 tok/s
vLLM generation done - elapsed time: 3.72s, prompts: 4, total tokens: 3819, avg length: 954.8, speed: 1025.2 tok/s
vLLM generation done - elapsed time: 3.76s, prompts: 4, total tokens: 4096, avg length: 1024.0, speed: 1089.7 tok/s
vLLM generation done - elapsed time: 3.65s, prompts: 4, total tokens: 2996, avg length: 749.0, speed: 821.5 tok/s
vLLM generation done - elapsed time: 3.68s, prompts: 4, total tokens: 3200, avg length: 800.0, speed: 869.8 tok/s
vLLM generation done - elapsed time: 3.68s, prompts: 4, total tokens: 3585, avg length: 896.2, speed: 973.6 tok/s
vLLM generation done - elapsed time: 3.73s, prompts: 4, total tokens: 3745, avg length: 936.2, speed: 1004.9 tok/s
18%|█▊ | 18/100 [02:40<12:04, 8.84s/it]
{'loss': 0.005, 'grad_norm': 0.2562546730041504, 'learning_rate': 4.15e-06, 'on_policy_loss': 0.005, 'rollout_accuracy': 0.5, 'epoch': 0.07}
18%|█▊ | 18/100 [02:40<12:04, 8.84s/it]vLLM generation done - elapsed time: 3.64s, prompts: 4, total tokens: 2818, avg length: 704.5, speed: 773.6 tok/s
vLLM generation done - elapsed time: 3.69s, prompts: 4, total tokens: 3056, avg length: 764.0, speed: 828.1 tok/s
vLLM generation done - elapsed time: 3.69s, prompts: 4, total tokens: 3255, avg length: 813.8, speed: 881.6 tok/s
vLLM generation done - elapsed time: 3.74s, prompts: 4, total tokens: 3702, avg length: 925.5, speed: 990.0 tok/s
vLLM generation done - elapsed time: 3.68s, prompts: 4, total tokens: 3618, avg length: 904.5, speed: 983.1 tok/s
vLLM generation done - elapsed time: 3.71s, prompts: 4, total tokens: 3508, avg length: 877.0, speed: 945.7 tok/s
vLLM generation done - elapsed time: 3.72s, prompts: 4, total tokens: 3587, avg length: 896.8, speed: 965.4 tok/s
vLLM generation done - elapsed time: 3.76s, prompts: 4, total tokens: 3934, avg length: 983.5, speed: 1047.3 tok/s
#48
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolexec
args
{
"input": "const r = await tools.write_stdin({session_id:35523,chars:\"\",yield_time_ms:50000,max_output_tokens:600});\ntext(r.output);"
}exec result
Script running with cell ID 36
Wall time 11.0 seconds
Output:
#49
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolwait
args
{
"cell_id": "36",
"yield_time_ms": 50000,
"max_tokens": 600
}wait result
Script completed
Wall time 36.0 seconds
Output:
Warning: truncated output (original token count: 627)
Total output lines: 21
Warning: truncated output (original token count: 2783)
Total output lines: 176
19%|█▉ | 19/100 [02:49<11:56, 8.85s/it]vLLM generation done - elapsed time: 3.64s, prompts: 4, total tokens: 2919, avg length: 729.8, speed: 802.3 tok/s
vLLM generation done - elapsed time: 3.73s, prompts: 4, total tokens: 3400, avg length: 850.0, speed: 911.8 tok/s
vLLM generation done - elapsed time: 3.75s, prompts: 4, total tokens: 3888, avg length: 972.0, speed: 1037.3 tok/s
vLLM generation done - elapsed time: 3.75s, prompts: 4, total tokens: 3572, avg length: 893.0, speed: 952.4 tok/s
vLLM generation done - elapsed time: 3.71s, prompts: 4, total tokens: 3355, avg length: 838.8, speed: 903.5 tok/s
vLLM generation done - elapsed time: 3.73s, prompts: 4, total tokens: 3433, avg length: 858.2, speed: 920.9 tok/s
vLLM generation done - elapsed time: 3.73s, prompts: 4, total tokens: 3629, avg length: 907.2, speed: 972.0 tok/s
vLLM generation done - elapsed time: 3.77s, prompts: 4, total tokens: 4019, avg length: 1004.8, speed: 1065.0 tok/s
20%|██ | 20/100 [02:58<11:50, 8.88s/it]
{'loss': 0.0063, 'grad_norm': 0.091454431…27 tokens truncated…
{'loss': 0.002, 'grad_norm': 0.05487716943025589, 'learning_rate': 3.85e-06, 'on_policy_loss': 0.002, 'rollout_accuracy': 0.1875, 'epoch': 0.09}
24%|██▍ | 24/100 [03:33<11:15, 8.88s/it]vLLM generation done - elapsed time: 3.24s, prompts: 4, total tokens: 2557, avg length: 639.2, speed: 789.3 tok/s
vLLM generation done - elapsed time: 3.64s, prompts: 4, total tokens: 3143, avg length: 785.8, speed: 863.2 tok/s
vLLM generation done - elapsed time: 3.68s, prompts: 4, total tokens: 3167, avg length: 791.8, speed: 861.1 tok/s
vLLM generation done - elapsed time: 3.71s, prompts: 4, total tokens: 3577, avg length: 894.2, speed: 964.1 tok/s
vLLM generation done - elapsed time: 3.47s, prompts: 4, total tokens: 2203, avg length: 550.8, speed: 634.0 tok/s
vLLM generation done - elapsed time: 3.69s, prompts: 4, total tokens: 3301, avg length: 825.2, speed: 894.4 tok/s
vLLM generation done - elapsed time: 3.75s, prompts: 4, total tokens: 4096, avg length: 1024.0, speed: 1092.5 tok/s
vLLM generation done - elapsed time: 3.76s, prompts: 4, total tokens: 4071, avg length: 1017.8, speed: 1083.0 tok/s
25%|██▌ | 25/100 [03:42<11:06, 8.88s/it]#50
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolexec
args
{
"input": "const r = await tools.write_stdin({session_id:35523,chars:\"\",yield_time_ms:50000,max_output_tokens:500});\ntext(r.output);"
}exec result
Script running with cell ID 37
Wall time 11.0 seconds
Output: