gpt-5.6-sol

{
  "cell_id": "214",
  "yield_time_ms": 30000,
  "max_tokens": 300
}

{
  "input": "const r = await tools.write_stdin({session_id:79122,chars:\"\",yield_time_ms:30000,max_output_tokens:300});\ntext(r.output);\n"
}

{
  "cell_id": "215",
  "yield_time_ms": 30000,
  "max_tokens": 300
}

{
  "input": "const r = await tools.write_stdin({session_id:79122,chars:\"\",yield_time_ms:30000,max_output_tokens:300});\ntext(r.output);\n"
}

{
  "cell_id": "216",
  "yield_time_ms": 30000,
  "max_tokens": 300
}

{
  "input": "const r = await tools.write_stdin({session_id:79122,chars:\"\",yield_time_ms:30000,max_output_tokens:300});\ntext(r.output);\n"
}

{
  "cell_id": "217",
  "yield_time_ms": 30000,
  "max_tokens": 300
}

{
  "input": "const r = await tools.write_stdin({session_id:79122,chars:\"\",yield_time_ms:30000,max_output_tokens:300});\ntext(r.output);\n"
}

{
  "cell_id": "218",
  "yield_time_ms": 30000,
  "max_tokens": 300
}

{
  "input": "const r = await tools.write_stdin({session_id:79122,chars:\"\",yield_time_ms:30000,max_output_tokens:300});\ntext(r.output);\n"
}

{
  "cell_id": "219",
  "yield_time_ms": 30000,
  "max_tokens": 300
}

{
  "input": "const r = await tools.write_stdin({session_id:79122,chars:\"\",yield_time_ms:30000,max_output_tokens:300});\ntext(r.output);\n"
}

{
  "cell_id": "220",
  "yield_time_ms": 30000,
  "max_tokens": 300
}

{
  "input": "const r = await tools.write_stdin({session_id:79122,chars:\"\",yield_time_ms:30000,max_output_tokens:300});\ntext(r.output);\n"
}

{
  "cell_id": "221",
  "yield_time_ms": 30000,
  "max_tokens": 300
}

{
  "input": "const r = await tools.write_stdin({session_id:79122,chars:\"\",yield_time_ms:30000,max_output_tokens:300});\ntext(r.output);\n"
}

{
  "cell_id": "222",
  "yield_time_ms": 30000,
  "max_tokens": 300
}

{
  "input": "const r = await tools.write_stdin({session_id:79122,chars:\"\",yield_time_ms:30000,max_output_tokens:300});\ntext(r.output);\n"
}

{
  "cell_id": "223",
  "yield_time_ms": 30000,
  "max_tokens": 300
}

{
  "input": "const r = await tools.write_stdin({session_id:79122,chars:\"\",yield_time_ms:30000,max_output_tokens:300});\ntext(r.output);\n"
}

{
  "cell_id": "224",
  "yield_time_ms": 30000,
  "max_tokens": 300
}

{
  "input": "const patch1 = \"*** Begin Patch\\n*** Update File: /workspace/repo/opsd_trainer.py\\n@@\\n-            token_clip:\\n-                If set, bounds the influence of each sequence token after\\n-                summing over the vocabulary. High-divergence tokens retain a\\n-                scaled gradient instead of being hard-clamped to zero.\\n+            token_clip:\\n+                if set, clips per-token divergence values to this maximum before reduction. Prevents style tokens from dominating the gradient signal over math tokens.\\n@@\\n-        # Aggregate vocabulary contributions into an actual divergence for\\n-        # each sequence token.  The released implementation clamps individual\\n-        # signed vocabulary summands before this sum.  That operation is\\n-        # asymmetric (positive terms are clipped, negative terms are not), can\\n-        # make a forward KL negative, and eventually trains away from its own\\n-        # teacher.  We instead use a detached influence cap on the complete\\n-        # token KL.  Easy/math tokens receive their full gradient; unusually\\n-        # large style/context-shift tokens receive a gradient scaled to the cap\\n-        # rather than no gradient at all.\\n-        token_divergence = jsd.sum(dim=-1)\\n+        # Per-token clipping: cap each token's divergence value\\n         if token_clip is not None:\\n-            influence = (token_clip / token_divergence.detach().clamp_min(token_clip)).clamp(max=1.0)\\n-            token_divergence = token_divergence * influence\\n+            jsd = jsd.clamp(max=token_clip)\\n \\n         # Masking\\n         if labels is not None:\\n             mask = labels != -100\\n-            token_divergence = token_divergence[mask]\\n+            jsd = jsd[mask]\\n \\n         # Apply reduction\\n         if reduction == \\\"batchmean\\\":\\n-            return (\\n-                token_divergence.sum() / mask.sum()\\n-                if labels is not None\\n-                else token_divergence.sum() / token_divergence.size(0)\\n-            )\\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*** End Patch\";\nconst patch2 = \"*** Begin Patch\\n*** Update File: /workspace/submission/recipe.env\\n@@\\n-max_completion_length=2048\\n+max_completion_length=1536\\n*** End Patch\";\ntext(await tools.apply_patch(patch1));\ntext(await tools.apply_patch(patch2));\n"
}