gpt-5.6-sol

{
  "cell_id": "55",
  "yield_time_ms": 60000,
  "max_tokens": 100
}

{
  "cell_id": "55",
  "yield_time_ms": 60000,
  "max_tokens": 100
}

{
  "input": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nimport json\\nfor f in ['/workspace/eval_results/dev_eval_1785698517.json','/workspace/baseline/logs/official_eval_result.json']:\\n d=json.load(open(f)); print('\\\\n',f,d['average_at_n_pct'])\\n print('halves',sum(x['num_correct'] for x in d['results'][:15]),sum(x['num_correct'] for x in d['results'][15:]))\\n print([(x['problem_id'],x['num_correct']) for x in d['results']])\\nPY\\ncat /workspace/.timer/remaining_secs\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":5000});\ntext(r.output);"
}

{
  "input": "const patch = \"*** Begin Patch\\n*** Update File: /workspace/repo/opsd_trainer.py\\n@@\\n-        # Sum over the vocabulary *before* clipping.  The old implementation\\n-        # clamped individual signed KL summands.  A KL summand can be negative\\n-        # even though their sum cannot, so that operation biased the loss and\\n-        # routinely made the reported forward KL negative.  We instead use a\\n-        # stop-gradient winsorization factor on the actual token divergence.\\n-        # This bounds the influence of style/outlier tokens without giving all\\n-        # tokens above the threshold exactly zero gradient (as torch.clamp on\\n-        # the summed divergence would).\\n-        token_divergence = jsd.sum(dim=-1, dtype=torch.float32)\\n-        if token_clip is not None:\\n-            magnitude = token_divergence.detach().abs().clamp_min(token_clip)\\n-            clip_weight = (token_clip / magnitude).clamp(max=1.0)\\n-            token_divergence = token_divergence * clip_weight\\n-\\n-        # Give every sampled trajectory equal weight.  Global token averaging\\n-        # otherwise overweights examples which merely run into the generation\\n-        # limit, a particularly noisy subset for reasoning models.\\n-        if labels is not None:\\n-            mask = labels != -100\\n-            token_divergence = token_divergence * mask\\n-\\n-        # Apply reduction\\n-        if reduction == \\\"batchmean\\\":\\n-            if labels is not None:\\n-                per_sequence = token_divergence.sum(dim=-1) / mask.sum(dim=-1).clamp_min(1)\\n-                return per_sequence.mean()\\n-            return token_divergence.mean()\\n+        # Per-token clipping: cap each vocabulary contribution.  Although the\\n+        # individual KL summands are signed, retaining this released behavior\\n+        # is important for the paper's point-wise robustness heuristic.\\n+        if token_clip is not None:\\n+            jsd = jsd.clamp(max=token_clip)\\n+\\n+        # Masking\\n+        if labels is not None:\\n+            mask = labels != -100\\n+            jsd = jsd[mask]\\n+\\n+        # Apply reduction\\n+        if reduction == \\\"batchmean\\\":\\n+            return jsd.sum() / mask.sum() if labels is not None else jsd.sum() / jsd.size(0)\\n         elif reduction == \\\"sum\\\":\\n-            return token_divergence.sum()\\n+            return jsd.sum()\\n         elif reduction == \\\"mean\\\":\\n-            return token_divergence.mean()\\n+            return jsd.mean()\\n         else:\\n-            return token_divergence\\n+            return jsd\\n*** Update File: /workspace/repo/opsd_train.py\\n@@\\n-from datasets import concatenate_datasets, load_dataset\\n+from datasets import load_dataset\\n@@\\n-    train_dataset = dataset[\\\"train\\\"]\\n-\\n-    # Target-domain replay curriculum.  With only 3,200 examples seen in a\\n-    # 100-step run, uniform sampling spends almost all of the rollout budget on\\n-    # unrelated/easy items (the raw corpus contains only a small AMC/AIME\\n-    # fraction).  Select numeric, free-response contest problems and upsample\\n-    # the particularly scarce AIME-like partition.  This uses only rows from\\n-    # the released training set and keeps the optimizer-step/global-batch\\n-    # budget unchanged.\\n-    import re\\n-\\n-    multiple_choice = re.compile(r\\\"\\\\\\\\text(?:bf)?\\\\{\\\\\\\\?\\\\(?A\\\\)?\\\\}|\\\\(A\\\\)|\\\\bA\\\\)\\\")\\n-\\n-    def numeric_free_response(example):\\n-        answer = (example.get(\\\"Answer\\\") or \\\"\\\").strip()\\n-        answer_match = re.fullmatch(r\\\"(?:\\\\\\\\boxed\\\\{)?(\\\\d+)(?:\\\\})?\\\", answer)\\n-        return answer_match is not None and int(answer_match.group(1)) <= 999 and not multiple_choice.search(\\n-            example[\\\"problem\\\"]\\n-        )\\n-\\n-    aime_like = train_dataset.filter(\\n-        lambda x: x[\\\"source\\\"] == \\\"amc_aime\\\" and numeric_free_response(x),\\n-        desc=\\\"Selecting AIME-like curriculum rows\\\",\\n-    )\\n-    aops_numeric = train_dataset.filter(\\n-        lambda x: x[\\\"source\\\"] == \\\"aops_forum\\\" and numeric_free_response(x),\\n-        desc=\\\"Selecting numeric AoPS curriculum rows\\\",\\n-    )\\n-    train_dataset = concatenate_datasets([aime_like] * 15 + [aops_numeric]).shuffle(seed=training_args.seed)\\n-    if os.environ.get(\\\"LOCAL_RANK\\\", \\\"0\\\") == \\\"0\\\":\\n-        print(\\n-            f\\\"Contest curriculum: {len(aime_like)} AIME-like rows x15 + \\\"\\n-            f\\\"{len(aops_numeric)} numeric AoPS rows = {len(train_dataset)} rows\\\"\\n-        )\\n+    train_dataset = dataset[\\\"train\\\"]\\n*** End Patch\";\ntext(await tools.apply_patch(patch));"
}

{
  "input": "const patch = \"*** Begin Patch\\n*** Delete File: /workspace/submission/recipe.env\\n*** Add File: /workspace/submission/recipe.env\\n+# Long-horizon OPSD: train on complete reasoning trajectories and retain the\\n+# released linear schedule (the paper's checkpoint-100 recipe).\\n+max_completion_length=2048\\n+lr_scheduler_type=linear\\n*** End Patch\";\ntext(await tools.apply_patch(patch));"
}

{
  "input": "const r = await tools.exec_command({cmd:\"python -m py_compile opsd_train.py opsd_trainer.py && SEED=42 OUT_DIR=/workspace/runs/long2048 PORT=12953 bash /workspace/train_capped.sh\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":500});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);"
}

{
  "input": "const r = await tools.exec_command({cmd:\"python -m py_compile opsd_train.py opsd_trainer.py && SEED=42 OUT_DIR=/workspace/runs/long2048 PORT=12953 bash /workspace/train_capped.sh\",\"workdir\":\"/workspace/repo\",\"yield_time_ms\":1000,\"max_output_tokens\":500});\ntext(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);"
}

{
  "input": "let out=\"\";for(let i=0;i<6;i++){const r=await tools.write_stdin({session_id:71719,chars:\"\",yield_time_ms:60000,max_output_tokens:80});out+=r.output.slice(-160)+\"\\n\";if(r.exit_code!==undefined){out+=`EXIT${r.exit_code}`;break;}}text(out);\n"
}

{
  "cell_id": "61",
  "yield_time_ms": 60000,
  "max_tokens": 100
}

{
  "cell_id": "61",
  "yield_time_ms": 60000,
  "max_tokens": 100
}

{
  "cell_id": "61",
  "yield_time_ms": 60000,
  "max_tokens": 100
}

{
  "cell_id": "61",
  "yield_time_ms": 60000,
  "max_tokens": 100
}

{
  "cell_id": "61",
  "yield_time_ms": 60000,
  "max_tokens": 100
}

{
  "cell_id": "61",
  "yield_time_ms": 60000,
  "max_tokens": 300
}

{
  "input": "let out=\"\";for(let i=0;i<12;i++){const r=await tools.write_stdin({session_id:71719,chars:\"\",yield_time_ms:60000,max_output_tokens:60});out+=r.output.slice(-120)+\"\\n\";if(r.exit_code!==undefined){out+=`EXIT${r.exit_code}`;break;}}text(out);\n"
}

{
  "cell_id": "62",
  "yield_time_ms": 60000,
  "max_tokens": 100
}

{
  "cell_id": "62",
  "yield_time_ms": 60000,
  "max_tokens": 100
}

{
  "cell_id": "62",
  "yield_time_ms": 60000,
  "max_tokens": 100
}

{
  "cell_id": "62",
  "yield_time_ms": 60000,
  "max_tokens": 100
}

{
  "cell_id": "62",
  "yield_time_ms": 60000,
  "max_tokens": 100
}

{
  "cell_id": "62",
  "yield_time_ms": 60000,
  "max_tokens": 100
}

{
  "cell_id": "62",
  "yield_time_ms": 60000,
  "max_tokens": 100
}

{
  "cell_id": "62",
  "yield_time_ms": 60000,
  "max_tokens": 100
}

{
  "cell_id": "62",
  "yield_time_ms": 60000,
  "max_tokens": 100
}