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: 428
duration: 9h 21m
recorded: 2026-08-02

run idopsd-aime24-4gpus__eNDoWrD

Everything gpt-5.6-sol did on this task, in order.

download JSON ↗

codexgpt-5.6-sol444 turns16 narration428 reasoning428 action428 observation1 final

#176

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec

args

{
  "input": "let out=\"\";for(let i=0;i<12;i++){const r=await tools.write_stdin({session_id:34388,chars:\"\",yield_time_ms:60000,max_output_tokens:50});out+=r.output.slice(-100)+\"\\n\";if(r.exit_code!==undefined){out+=`EXIT${r.exit_code}`;break;}}text(out);\n"
}

exec result

Script running with cell ID 66
Wall time 11.0 seconds
Output:

#177

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait

args

{
  "cell_id": "66",
  "yield_time_ms": 60000,
  "max_tokens": 100
}

wait result

Script running with cell ID 66
Wall time 61.0 seconds
Output:

#178

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait

args

{
  "cell_id": "66",
  "yield_time_ms": 60000,
  "max_tokens": 100
}

wait result

Script running with cell ID 66
Wall time 61.0 seconds
Output:

#179

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait

args

{
  "cell_id": "66",
  "yield_time_ms": 60000,
  "max_tokens": 100
}

wait result

Script running with cell ID 66
Wall time 61.0 seconds
Output:

#180

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait

args

{
  "cell_id": "66",
  "yield_time_ms": 60000,
  "max_tokens": 100
}

wait result

Script running with cell ID 66
Wall time 61.0 seconds
Output:

#181

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait

args

{
  "cell_id": "66",
  "yield_time_ms": 60000,
  "max_tokens": 100
}

wait result

Script completed
Wall time 16.4 seconds
Output:
Warning: truncated output (original token count: 136)
Total output lines: 8

uncated…███▌ | 72/84 [12:45<01:34,  7.86s/it, est. speed input: 9.94 toks/s, output: 1602.32 toks/s]
cated…██     | 48/96 [13:40<08:33, 10.69s/it, est. speed input: 8.73 toks/s, output…36 tokens truncated…▌  | 72/96 [15:34<03:02,  7.59s/it, est. speed input: 10.35 toks/s, output: 1474.99 toks/s]
lutions correct on 30 problems)
Results written to /workspace/eval_results/dev_eval_1785700951.json

EXIT0

#182

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec

args

{
  "input": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nimport json\\nf='/workspace/eval_results/dev_eval_1785700951.json';d=json.load(open(f));print(d['average_at_n_pct']);print(sum(x['num_correct'] for x in d['results'][:15]),sum(x['num_correct'] for x in d['results'][15:]));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\":3000});\ntext(r.output);"
}

exec result

Script completed
Wall time 0.1 seconds
Output:
54.44444444444444
117 79
[(60, 12), (61, 11), (62, 0), (63, 0), (64, 6), (65, 4), (66, 11), (67, 12), (68, 12), (69, 12), (70, 5), (71, 12), (72, 12), (73, 0), (74, 8), (75, 4), (76, 9), (77, 0), (78, 6), (79, 12), (80, 4), (81, 0), (82, 8), (83, 11), (84, 12), (85, 0), (86, 10), (87, 3), (88, 0), (89, 0)]
14870

#183

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec

args

{
  "input": "const r = await tools.exec_command({cmd:\"sed -n '1460,1530p' opsd_trainer.py\",\"workdir\":\"/workspace/repo\",\"yield_time_ms\":10000,\"max_output_tokens\":5000});\ntext(r.output);"
}

exec result

Script completed
Wall time 0.1 seconds
Output:
        else:
            self._off_policy_loss_total += loss_scalar
            self._off_policy_step_equiv += step_equiv
        return loss

    def log(self, logs: dict[str, float], start_time: float | None = None) -> None:
        mode = "train" if self.model.training else "eval"
        metrics = {
            key: sum(val) / len(val) for key, val in self._metrics[mode].items()
        }  # average the metrics

        if mode == "train":
            device = self.accelerator.device if hasattr(self.accelerator, "device") else torch.device("cpu")
            # Track on/off-policy loss statistics
            vec = torch.tensor(
                [
                    self._on_policy_loss_total,
                    self._off_policy_loss_total,
                    self._on_policy_step_equiv,
                    self._off_policy_step_equiv,
                ],
                dtype=torch.float64,
                device=device,
            )

            # Sum across processes so we mirror Trainer's distributed reduction
            if (
                getattr(self.accelerator, "distributed_type", DistributedType.NO) != DistributedType.NO
                and dist.is_available()
                and dist.is_initialized()
            ):
                dist.all_reduce(vec, op=dist.ReduceOp.SUM)

            (
                on_sum,
                off_sum,
                on_eq,
                off_eq,
            ) = vec.tolist()

            # Compute category averages over the *same window* as Trainer's logs
            # (avoid div-by-zero if, e.g., no on-policy steps in the window)
            if on_eq > 0:
                logs["on_policy_loss"] = round(on_sum / on_eq, 4)
            if off_eq > 0:
                logs["off_policy_loss"] = round(off_sum / off_eq, 4)

            # Reset window accumulators after logging (just like Trainer resets its window)
            self._on_policy_loss_total = self._off_policy_loss_total = 0.0
            self._on_policy_step_equiv = self._off_policy_step_equiv = 0.0

        # This method can be called both in training and evaluation. When called in evaluation, the keys in `logs`
        # start with "eval_". We need to add the prefix "eval_" to the keys in `metrics` to match the format.
        if mode == "eval":
            metrics = {f"eval_{key}": val for key, val in metrics.items()}

        logs = {**logs, **metrics}
        super().log(logs, start_time)
        self._metrics[mode].clear()

        if (
            self.accelerator.is_main_process
            and self.log_completions
            and ((self.state.global_step % self.log_completion_steps) == 0)
        ):

            if self.args.report_to and "wandb" in self.args.report_to and wandb.run is not None:
                import pandas as pd

                table = {
                    "step": [str(self.state.global_step)] * len(self._textual_logs["prompt"]),

#184

narration

The full long-horizon result is 54.44%—essentially neutral versus the 54.22 anchor. I’m keeping the useful complete-trajectory option in reserve, but the next candidate addresses a deeper weakness: OPSD only teaches on the student’s own path, so an incorrect rollout never exposes the student to a correct trajectory. I’m adding a small ground-truth trajectory anchor alongside the on-policy KL, using the same in-dataset solution already shown to the teacher. The coefficient will be low so OPSD remains the dominant objective.

#185

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         student_prompts = []\\n         teacher_prompts = []\\n+        anchor_sequences = []\\n@@\\n             student_prompts.append(student_prompt)\\n+\\n+            # Expert trajectory used as a weak anchor for OPSD.  It starts at\\n+            # exactly the same non-privileged student prompt; unlike the\\n+            # teacher context below it never exposes the solution in the user\\n+            # message.  The reference solution is available in every row of\\n+            # the released OPSD dataset.\\n+            anchor_sequences.append(student_prompt + solution.strip() + self.tokenizer.eos_token)\\n@@\\n         result = {\\n@@\\n         }\\n+\\n+        anchor_encoded = self.tokenizer(\\n+            anchor_sequences,\\n+            padding=True,\\n+            truncation=True,\\n+            max_length=self.max_length,\\n+            return_tensors=\\\"pt\\\",\\n+        )\\n+        anchor_labels = anchor_encoded[\\\"input_ids\\\"].clone()\\n+        for i, prompt_len in enumerate(student_prompt_lengths):\\n+            anchor_labels[i, :prompt_len] = -100\\n+        anchor_labels[anchor_encoded[\\\"attention_mask\\\"] == 0] = -100\\n+        result.update(\\n+            {\\n+                \\\"anchor_input_ids\\\": anchor_encoded[\\\"input_ids\\\"],\\n+                \\\"anchor_attention_mask\\\": anchor_encoded[\\\"attention_mask\\\"],\\n+                \\\"anchor_labels\\\": anchor_labels,\\n+            }\\n+        )\\n*** Update File: /workspace/repo/opsd_trainer.py\\n@@\\n         else:\\n             # Temperature is applied inside generalized_jsd_loss\\n-            loss = self.generalized_jsd_loss(\\n+            opsd_loss = self.generalized_jsd_loss(\\n                 student_logits=student_logits_for_loss,\\n@@\\n             )\\n             del student_logits_for_loss, teacher_logits_for_loss\\n+\\n+            # OPSD only visits student trajectories.  A weak maximum-\\n+            # likelihood anchor on the known-correct trajectory prevents\\n+            # self-distillation from polishing an incorrect branch forever.\\n+            # `lmbda` is the anchor coefficient; setting it to zero recovers\\n+            # pure OPSD exactly.\\n+            if self.lmbda > 0 and \\\"anchor_input_ids\\\" in inputs:\\n+                anchor_outputs = model(\\n+                    input_ids=inputs[\\\"anchor_input_ids\\\"],\\n+                    attention_mask=inputs[\\\"anchor_attention_mask\\\"],\\n+                    labels=inputs[\\\"anchor_labels\\\"],\\n+                )\\n+                anchor_loss = anchor_outputs.loss\\n+                loss = opsd_loss + self.lmbda * anchor_loss\\n+                self._metrics[\\\"train\\\"][\\\"opsd_kl\\\"].append(float(opsd_loss.detach()))\\n+                self._metrics[\\\"train\\\"][\\\"anchor_ce\\\"].append(float(anchor_loss.detach()))\\n+                del anchor_outputs\\n+            else:\\n+                loss = opsd_loss\\n*** Update File: /workspace/submission/recipe.env\\n@@\\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+# Trajectory-anchored OPSD.  A small correct-path likelihood term complements\\n+# the on-policy privileged-teacher KL without overwhelming it.\\n+lmbda=0.02\\n+max_completion_length=1024\\n lr_scheduler_type=linear\\n*** End Patch\";\ntext(await tools.apply_patch(patch));"
}

exec result

Script completed
Wall time 0.0 seconds
Output:
{}

#186

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 && python - <<'PY'\\nfrom transformers import AutoTokenizer\\nfrom datasets import load_dataset\\nfrom data_collator import SelfDistillationDataCollator\\nt=AutoTokenizer.from_pretrained('/opt/models/Qwen3-1.7B');D=load_dataset('siyanzhao/Openthoughts_math_30k_opsd')['train']\\nx=SelfDistillationDataCollator(t,reason_first=False)([D[0],D[1]])\\nfor k,v in x.items():\\n if hasattr(v,'shape'): print(k,tuple(v.shape))\\nprint(t.decode(x['anchor_input_ids'][0][x['anchor_labels'][0]!=-100])[-200:])\\nPY\",\"workdir\":\"/workspace/repo\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(r.output);"
}

exec result

Script completed
Wall time 5.2 seconds
Output:
Using the latest cached version of the dataset since siyanzhao/Openthoughts_math_30k_opsd couldn't be found on the Hugging Face Hub (offline mode is enabled).
Found the latest cached dataset configuration 'default' at /opt/hf_cache/datasets/siyanzhao___openthoughts_math_30k_opsd/default/0.0.0/1f33e9dc2e8a1c639ca74f8024ad4a9f1f5eae62 (last modified on Sun Aug  2 19:00:46 2026).
[DataCollator] Original padding_side: right
[DataCollator] Set padding_side to: right
[DataCollator] Reason first mode: False
student_prompts (2, 164)
student_prompt_attention_mask (2, 164)
student_prompt_lengths_per_example (2,)
anchor_input_ids (2, 1623)
anchor_attention_mask (2, 1623)
anchor_labels (2, 1623)
teacher_prompts (2, 1710)
teacher_prompt_attention_mask (2, 1710)
teacher_prompt_lengths_per_example (2,)
 27c - 9ab}{\lambda^3} = -\frac{27}{4} \left(-\frac{2\sqrt{3}}{9}\right) = \frac{27 \times 2 \sqrt{3}}{4 \times 9} = \frac{3\sqrt{3}}{2}.
   \]

Conclusion:
\[
\boxed{\frac{3\sqrt{3}}{2}}
\]<|im_end|>

#187

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec

args

{
  "input": "const r = await tools.exec_command({cmd:\"SEED=42 OUT_DIR=/workspace/runs/anchor02 PORT=12954 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}`);"
}

exec result

Script completed
Wall time 1.2 seconds
Output:
SESSION_ID=78778

#188

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec

args

{
  "input": "let out=\"\";for(let i=0;i<6;i++){const r=await tools.write_stdin({session_id:78778,chars:\"\",yield_time_ms:60000,max_output_tokens:100});out+=r.output.slice(-220)+\"\\n\";if(r.exit_code!==undefined){out+=`EXIT${r.exit_code}`;break;}}text(out);\n"
}

exec result

Script running with cell ID 72
Wall time 11.0 seconds
Output:

#189

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait

args

{
  "cell_id": "72",
  "yield_time_ms": 60000,
  "max_tokens": 150
}

wait result

Script completed
Wall time 38.2 seconds
Output:
73 tokens truncated… To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
TRAIN_CKPT=
[train_capped] FATAL: no checkpoint-100 produced

EXIT4

#190

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec

args

{
  "input": "const r = await tools.exec_command({cmd:\"tail -180 /workspace/runs/anchor02/train_seed42.log | sed -n '1,180p'\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":20000});\ntext(r.output);"
}

exec result

Script completed
Wall time 0.1 seconds
Output:
[rank2]:   File "/usr/local/lib/python3.10/dist-packages/transformers/trainer.py", line 2325, in train
[rank2]:     return inner_training_loop(
[rank2]:   File "/usr/local/lib/python3.10/dist-packages/transformers/trainer.py", line 2674, in _inner_training_loop
[rank2]:     tr_loss_step = self.training_step(model, inputs, num_items_in_batch)
[rank2]:   File "/usr/local/lib/python3.10/dist-packages/trl/extras/profiling.py", line 98, in wrapper
[rank2]:     return func(self, *args, **kwargs)
[rank2]:   File "/workspace/repo/opsd_trainer.py", line 1462, in training_step
[rank2]:     loss = super().training_step(model, inputs, num_items_in_batch)
[rank2]:   File "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py", line 1245, in training_step
[rank2]:     return super().training_step(*args, **kwargs)
[rank2]:   File "/usr/local/lib/python3.10/dist-packages/transformers/trainer.py", line 4071, in training_step
[rank2]:     self.accelerator.backward(loss, **kwargs)
[rank2]:   File "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py", line 2732, in backward
[rank2]:     self.deepspeed_engine_wrapped.backward(loss, sync_gradients=self.sync_gradients, **kwargs)
[rank2]:   File "/usr/local/lib/python3.10/dist-packages/accelerate/utils/deepspeed.py", line 270, in backward
[rank2]:     self.engine.backward(loss, **kwargs)
[rank2]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/utils/nvtx.py", line 20, in wrapped_fn
[rank2]:     ret_val = func(*args, **kwargs)
[rank2]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/engine.py", line 2357, in backward
[rank2]:     self._backward_epilogue()
[rank2]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/engine.py", line 2288, in _backward_epilogue
[rank2]:     self.allreduce_gradients()
[rank2]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/utils/nvtx.py", line 20, in wrapped_fn
[rank2]:     ret_val = func(*args, **kwargs)
[rank2]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/engine.py", line 2239, in allreduce_gradients
[rank2]:     self.optimizer.overlapping_partition_gradients_reduce_epilogue()
[rank2]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/zero/stage_1_and_2.py", line 947, in overlapping_partition_gradients_reduce_epilogue
[rank2]:     self.independent_gradient_partition_epilogue()
[rank2]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/zero/stage_1_and_2.py", line 839, in independent_gradient_partition_epilogue
[rank2]:     self.reduce_ipg_grads()
[rank2]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/zero/stage_1_and_2.py", line 1499, in reduce_ipg_grads
[rank2]:     assert self.params_already_reduced[param_id] == False, \
[rank2]: AssertionError: The parameter 391 has already been reduced.                         Gradient computed twice for this partition.                         Multiple gradient reduction is currently not supported
[rank0]: Traceback (most recent call last):
[rank0]:   File "/workspace/repo/opsd_train.py", line 296, in <module>
[rank0]:     trainer.train()
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/transformers/trainer.py", line 2325, in train
[rank0]:     return inner_training_loop(
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/transformers/trainer.py", line 2674, in _inner_training_loop
[rank0]:     tr_loss_step = self.training_step(model, inputs, num_items_in_batch)
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/trl/extras/profiling.py", line 98, in wrapper
[rank0]:     return func(self, *args, **kwargs)
[rank0]:   File "/workspace/repo/opsd_trainer.py", line 1462, in training_step
[rank0]:     loss = super().training_step(model, inputs, num_items_in_batch)
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py", line 1245, in training_step
[rank0]:     return super().training_step(*args, **kwargs)
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/transformers/trainer.py", line 4071, in training_step
[rank0]:     self.accelerator.backward(loss, **kwargs)
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py", line 2732, in backward
[rank0]:     self.deepspeed_engine_wrapped.backward(loss, sync_gradients=self.sync_gradients, **kwargs)
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/accelerate/utils/deepspeed.py", line 270, in backward
[rank0]:     self.engine.backward(loss, **kwargs)
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/utils/nvtx.py", line 20, in wrapped_fn
[rank0]:     ret_val = func(*args, **kwargs)
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/engine.py", line 2357, in backward
[rank0]:     self._backward_epilogue()
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/engine.py", line 2288, in _backward_epilogue
[rank0]:     self.allreduce_gradients()
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/utils/nvtx.py", line 20, in wrapped_fn
[rank0]:     ret_val = func(*args, **kwargs)
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/engine.py", line 2239, in allreduce_gradients
[rank0]:     self.optimizer.overlapping_partition_gradients_reduce_epilogue()
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/zero/stage_1_and_2.py", line 947, in overlapping_partition_gradients_reduce_epilogue
[rank0]:     self.independent_gradient_partition_epilogue()
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/zero/stage_1_and_2.py", line 839, in independent_gradient_partition_epilogue
[rank0]:     self.reduce_ipg_grads()
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/zero/stage_1_and_2.py", line 1499, in reduce_ipg_grads
[rank0]:     assert self.params_already_reduced[param_id] == False, \
[rank0]: AssertionError: The parameter 391 has already been reduced.                         Gradient computed twice for this partition.                         Multiple gradient reduction is currently not supported
[rank3]: Traceback (most recent call last):
[rank3]:   File "/workspace/repo/opsd_train.py", line 296, in <module>
[rank3]:     trainer.train()
[rank3]:   File "/usr/local/lib/python3.10/dist-packages/transformers/trainer.py", line 2325, in train
[rank3]:     return inner_training_loop(
[rank3]:   File "/usr/local/lib/python3.10/dist-packages/transformers/trainer.py", line 2674, in _inner_training_loop
[rank3]:     tr_loss_step = self.training_step(model, inputs, num_items_in_batch)
[rank3]:   File "/usr/local/lib/python3.10/dist-packages/trl/extras/profiling.py", line 98, in wrapper
[rank3]:     return func(self, *args, **kwargs)
[rank3]:   File "/workspace/repo/opsd_trainer.py", line 1462, in training_step
[rank3]:     loss = super().training_step(model, inputs, num_items_in_batch)
[rank3]:   File "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py", line 1245, in training_step
[rank3]:     return super().training_step(*args, **kwargs)
[rank3]:   File "/usr/local/lib/python3.10/dist-packages/transformers/trainer.py", line 4071, in training_step
[rank3]:     self.accelerator.backward(loss, **kwargs)
[rank3]:   File "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py", line 2732, in backward
[rank3]:     self.deepspeed_engine_wrapped.backward(loss, sync_gradients=self.sync_gradients, **kwargs)
[rank3]:   File "/usr/local/lib/python3.10/dist-packages/accelerate/utils/deepspeed.py", line 270, in backward
[rank3]:     self.engine.backward(loss, **kwargs)
[rank3]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/utils/nvtx.py", line 20, in wrapped_fn
[rank3]:     ret_val = func(*args, **kwargs)
[rank3]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/engine.py", line 2357, in backward
[rank3]:     self._backward_epilogue()
[rank3]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/engine.py", line 2288, in _backward_epilogue
[rank3]:     self.allreduce_gradients()
[rank3]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/utils/nvtx.py", line 20, in wrapped_fn
[rank3]:     ret_val = func(*args, **kwargs)
[rank3]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/engine.py", line 2239, in allreduce_gradients
[rank3]:     self.optimizer.overlapping_partition_gradients_reduce_epilogue()
[rank3]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/zero/stage_1_and_2.py", line 947, in overlapping_partition_gradients_reduce_epilogue
[rank3]:     self.independent_gradient_partition_epilogue()
[rank3]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/zero/stage_1_and_2.py", line 839, in independent_gradient_partition_epilogue
[rank3]:     self.reduce_ipg_grads()
[rank3]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/zero/stage_1_and_2.py", line 1499, in reduce_ipg_grads
[rank3]:     assert self.params_already_reduced[param_id] == False, \
[rank3]: AssertionError: The parameter 391 has already been reduced.                         Gradient computed twice for this partition.                         Multiple gradient reduction is currently not supported
[rank1]: Traceback (most recent call last):
[rank1]:   File "/workspace/repo/opsd_train.py", line 296, in <module>
[rank1]:     trainer.train()
[rank1]:   File "/usr/local/lib/python3.10/dist-packages/transformers/trainer.py", line 2325, in train
[rank1]:     return inner_training_loop(
[rank1]:   File "/usr/local/lib/python3.10/dist-packages/transformers/trainer.py", line 2674, in _inner_training_loop
[rank1]:     tr_loss_step = self.training_step(model, inputs, num_items_in_batch)
[rank1]:   File "/usr/local/lib/python3.10/dist-packages/trl/extras/profiling.py", line 98, in wrapper
[rank1]:     return func(self, *args, **kwargs)
[rank1]:   File "/workspace/repo/opsd_trainer.py", line 1462, in training_step
[rank1]:     loss = super().training_step(model, inputs, num_items_in_batch)
[rank1]:   File "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py", line 1245, in training_step
[rank1]:     return super().training_step(*args, **kwargs)
[rank1]:   File "/usr/local/lib/python3.10/dist-packages/transformers/trainer.py", line 4071, in training_step
[rank1]:     self.accelerator.backward(loss, **kwargs)
[rank1]:   File "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py", line 2732, in backward
[rank1]:     self.deepspeed_engine_wrapped.backward(loss, sync_gradients=self.sync_gradients, **kwargs)
[rank1]:   File "/usr/local/lib/python3.10/dist-packages/accelerate/utils/deepspeed.py", line 270, in backward
[rank1]:     self.engine.backward(loss, **kwargs)
[rank1]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/utils/nvtx.py", line 20, in wrapped_fn
[rank1]:     ret_val = func(*args, **kwargs)
[rank1]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/engine.py", line 2357, in backward
[rank1]:     self._backward_epilogue()
[rank1]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/engine.py", line 2288, in _backward_epilogue
[rank1]:     self.allreduce_gradients()
[rank1]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/utils/nvtx.py", line 20, in wrapped_fn
[rank1]:     ret_val = func(*args, **kwargs)
[rank1]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/engine.py", line 2239, in allreduce_gradients
[rank1]:     self.optimizer.overlapping_partition_gradients_reduce_epilogue()
[rank1]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/zero/stage_1_and_2.py", line 947, in overlapping_partition_gradients_reduce_epilogue
[rank1]:     self.independent_gradient_partition_epilogue()
[rank1]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/zero/stage_1_and_2.py", line 839, in independent_gradient_partition_epilogue
[rank1]:     self.reduce_ipg_grads()
[rank1]:   File "/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/zero/stage_1_and_2.py", line 1499, in reduce_ipg_grads
[rank1]:     assert self.params_already_reduced[param_id] == False, \
[rank1]: AssertionError: The parameter 391 has already been reduced.                         Gradient computed twice for this partition.                         Multiple gradient reduction is currently not supported
[rank0]:[W802 20:21:38.001518572 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank1]:[W802 20:21:39.803337901 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank2]:[W802 20:21:39.864048181 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank3]:[W802 20:21:39.864108365 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
W0802 20:21:45.180000 16133 torch/distributed/elastic/multiprocessing/api.py:900] Sending process 16183 closing signal SIGTERM
W0802 20:21:45.182000 16133 torch/distributed/elastic/multiprocessing/api.py:900] Sending process 16184 closing signal SIGTERM
W0802 20:21:45.182000 16133 torch/distributed/elastic/multiprocessing/api.py:900] Sending process 16185 closing signal SIGTERM
E0802 20:21:45.927000 16133 torch/distributed/elastic/multiprocessing/api.py:874] failed (exitcode: 1) local_rank: 0 (pid: 16182) of binary: /usr/bin/python3
Traceback (most recent call last):
  File "/usr/local/bin/accelerate", line 6, in <module>
    sys.exit(main())
  File "/usr/local/lib/python3.10/dist-packages/accelerate/commands/accelerate_cli.py", line 50, in main
    args.func(args)
  File "/usr/local/lib/python3.10/dist-packages/accelerate/commands/launch.py", line 1220, in launch_command
    deepspeed_launcher(args)
  File "/usr/local/lib/python3.10/dist-packages/accelerate/commands/launch.py", line 906, in deepspeed_launcher
    distrib_run.run(args)
  File "/usr/local/lib/python3.10/dist-packages/torch/distributed/run.py", line 892, in run
    elastic_launch(
  File "/usr/local/lib/python3.10/dist-packages/torch/distributed/launcher/api.py", line 143, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/usr/local/lib/python3.10/dist-packages/torch/distributed/launcher/api.py", line 277, in launch_agent
    raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
============================================================
opsd_train.py FAILED
------------------------------------------------------------
Failures:
  <NO_OTHER_FAILURES>
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2026-08-02_20:21:45
  host      : localhost
  rank      : 0 (local_rank: 0)
  exitcode  : 1 (pid: 16182)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================

#191

narration

The anchor prototype exposed a ZeRO-2 limitation—two gradient-bearing forwards through the same LoRA parameters in one backward pass are rejected. This is an implementation issue, not an OOM or method failure. I’m fixing it by batching the on-policy and correct trajectories into one student forward, then splitting the logits for KL and anchored likelihood; that is mathematically equivalent and compatible with the scorer’s distributed setup.

#192

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec

args

{
  "input": "const patch = \"*** Begin Patch\\n*** Update File: /workspace/repo/opsd_trainer.py\\n@@\\n-        # === STUDENT FORWARD - Extract log-probs immediately ===\\n-        outputs_student = model(\\n-            input_ids=inputs[\\\"student_input_ids\\\"],\\n-            attention_mask=inputs[\\\"student_attention_mask\\\"],\\n-        )\\n+        # === STUDENT FORWARD - Extract log-probs immediately ===\\n+        # Put the on-policy and correct anchor trajectories in one forward.\\n+        # Besides being cheaper than two calls, this is required by ZeRO-2,\\n+        # whose overlapping reducer cannot visit the same LoRA parameter from\\n+        # two checkpointed graphs in one backward pass.\\n+        use_anchor = self.lmbda > 0 and \\\"anchor_input_ids\\\" in inputs and not self.use_thinking_machines_loss\\n+        student_batch_size = inputs[\\\"student_input_ids\\\"].shape[0]\\n+        if use_anchor:\\n+            student_seq_len = inputs[\\\"student_input_ids\\\"].shape[1]\\n+            anchor_seq_len = inputs[\\\"anchor_input_ids\\\"].shape[1]\\n+            combined_seq_len = max(student_seq_len, anchor_seq_len)\\n+            pad_id = self.processing_class.pad_token_id\\n+            student_ids = F.pad(inputs[\\\"student_input_ids\\\"], (0, combined_seq_len - student_seq_len), value=pad_id)\\n+            student_mask = F.pad(inputs[\\\"student_attention_mask\\\"], (0, combined_seq_len - student_seq_len), value=0)\\n+            anchor_ids = F.pad(inputs[\\\"anchor_input_ids\\\"], (0, combined_seq_len - anchor_seq_len), value=pad_id)\\n+            anchor_mask = F.pad(inputs[\\\"anchor_attention_mask\\\"], (0, combined_seq_len - anchor_seq_len), value=0)\\n+            forward_ids = torch.cat([student_ids, anchor_ids], dim=0)\\n+            forward_mask = torch.cat([student_mask, anchor_mask], dim=0)\\n+        else:\\n+            forward_ids = inputs[\\\"student_input_ids\\\"]\\n+            forward_mask = inputs[\\\"student_attention_mask\\\"]\\n+\\n+        outputs_student = model(input_ids=forward_ids, attention_mask=forward_mask)\\n@@\\n-        student_logits = outputs_student.logits[:, student_prompt_len - 1 : -1, :]\\n+        student_logits = outputs_student.logits[:student_batch_size, student_prompt_len - 1 : student_seq_len - 1, :]\\n+\\n+        if use_anchor:\\n+            anchor_logits = outputs_student.logits[student_batch_size:, :anchor_seq_len, :]\\n+            anchor_loss = F.cross_entropy(\\n+                anchor_logits[:, :-1, :].reshape(-1, anchor_logits.shape[-1]),\\n+                inputs[\\\"anchor_labels\\\"][:, 1:].reshape(-1),\\n+                ignore_index=-100,\\n+            )\\n@@\\n-            if self.lmbda > 0 and \\\"anchor_input_ids\\\" in inputs:\\n-                anchor_outputs = model(\\n-                    input_ids=inputs[\\\"anchor_input_ids\\\"],\\n-                    attention_mask=inputs[\\\"anchor_attention_mask\\\"],\\n-                    labels=inputs[\\\"anchor_labels\\\"],\\n-                )\\n-                anchor_loss = anchor_outputs.loss\\n+            if use_anchor:\\n                 loss = opsd_loss + self.lmbda * anchor_loss\\n@@\\n-                del anchor_outputs\\n             else:\\n*** End Patch\";\ntext(await tools.apply_patch(patch));"
}

exec result

Script completed
Wall time 0.0 seconds
Output:
{}

#193

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec

args

{
  "input": "const patch = \"*** Begin Patch\\n*** Update File: /workspace/repo/opsd_trainer.py\\n@@\\n         # whose overlapping reducer cannot visit the same LoRA parameter from\\n         # two checkpointed graphs in one backward pass.\\n         use_anchor = self.lmbda > 0 and \\\"anchor_input_ids\\\" in inputs and not self.use_thinking_machines_loss\\n         student_batch_size = inputs[\\\"student_input_ids\\\"].shape[0]\\n+        student_seq_len = inputs[\\\"student_input_ids\\\"].shape[1]\\n         if use_anchor:\\n-            student_seq_len = inputs[\\\"student_input_ids\\\"].shape[1]\\n             anchor_seq_len = inputs[\\\"anchor_input_ids\\\"].shape[1]\\n*** End Patch\";\ntext(await tools.apply_patch(patch));"
}

exec result

Script completed
Wall time 0.0 seconds
Output:
{}

#194

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec

args

{
  "input": "const r = await tools.exec_command({cmd:\"python -m py_compile opsd_trainer.py && SEED=42 OUT_DIR=/workspace/runs/anchor02b PORT=12955 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}`);"
}

exec result

Script completed
Wall time 1.2 seconds
Output:
SESSION_ID=50612

#195

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec

args

{
  "input": "let out=\"\";for(let i=0;i<6;i++){const r=await tools.write_stdin({session_id:50612,chars:\"\",yield_time_ms:60000,max_output_tokens:100});out+=r.output.slice(-200)+\"\\n\";if(r.exit_code!==undefined){out+=`EXIT${r.exit_code}`;break;}}text(out);\n"
}

exec result

Script running with cell ID 77
Wall time 11.0 seconds
Output:

#196

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait

args

{
  "cell_id": "77",
  "yield_time_ms": 60000,
  "max_tokens": 150
}

wait result

Script running with cell ID 77
Wall time 61.0 seconds
Output:

#197

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait

args

{
  "cell_id": "77",
  "yield_time_ms": 60000,
  "max_tokens": 150
}

wait result

Script running with cell ID 77
Wall time 61.0 seconds
Output:

#198

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait

args

{
  "cell_id": "77",
  "yield_time_ms": 60000,
  "max_tokens": 150
}

wait result

Script running with cell ID 77
Wall time 61.0 seconds
Output:

#199

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait

args

{
  "cell_id": "77",
  "yield_time_ms": 60000,
  "max_tokens": 150
}

wait result

Script running with cell ID 77
Wall time 61.0 seconds
Output:

#200

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait

args

{
  "cell_id": "77",
  "yield_time_ms": 60000,
  "max_tokens": 150
}

wait result

Script running with cell ID 77
Wall time 61.0 seconds
Output: