claude-opus-5

495	            elif reduction == "sum":
496	                return jsd.sum()
497	            elif reduction == "mean":
498	                return jsd.mean()
499	            return jsd
500	
501	        # Per-token clipping: cap each token's divergence value
502	        if token_clip is not None:
503	            jsd = jsd.clamp(max=token_clip)
504	
505	        # Masking
506	        if labels is not None:
507	            mask = labels != -100
508	            jsd = jsd[mask]
509	
510	        # Apply reduction
511	        if reduction == "batchmean":
512	            return jsd.sum() / mask.sum() if labels is not None else jsd.sum() / jsd.size(0)
513	        elif reduction == "sum":
514	            return jsd.sum()

[metadata] {"type": "text", "file": {"filePath": "/workspace/repo/opsd_trainer.py", "content": "            elif reduction == \"sum\":\n                return jsd.sum()\n            elif reduction == \"mean\":\n                return jsd.mean()\n            return jsd\n\n        # Per-token clipping: cap each token's divergence value\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 jsd.sum()", "numLines": 20, "startLine": 495, "totalLines": 1721}}

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

[metadata] {"filePath": "/workspace/repo/opsd_trainer.py", "oldString": "        # Per-token clipping: cap each token's divergence value\n        if token_clip is not None:\n            jsd = jsd.clamp(max=token_clip)\n\n        # Masking", "newString": "        # Per-token clipping: cap each token's divergence value\n        if token_clip is not None:\n            if neg_gain != 1.0:\n                # --- Asymmetric pointwise clipping ------------------------------------\n                # The clipped forward KL is the sum of two very different halves. The\n                # positive entries (p_T > p_S) pull the student *toward* the privileged\n                # teacher; the clamp deliberately truncates them. The negative entries\n                # (p_S > p_T) push mass *off* everything the teacher does not endorse,\n                # i.e. they concentrate the student on its own teacher-endorsed choices.\n                # Measurements on this setup show the surviving objective is dominated by\n                # that second half, and it is what raises per-sample reliability (avg@k)\n                # rather than coverage. `neg_gain` re-balances the two halves explicitly\n                # instead of leaving the ratio to be an accident of the clip threshold.\n                clipped = jsd.clamp(max=token_clip)\n                jsd = torch.where(jsd < 0, neg_gain * clipped, clipped)\n            else:\n                jsd = jsd.clamp(max=token_clip)\n\n        # Masking", "originalFile": null, "structuredPatch": [{"oldStart": 500, "oldLines": 7, "newStart": 500, "newLines": 21, "lines": [" ", "         # Per-token clipping: cap each token's divergence value", "         if token_clip is not None:", "-            jsd = jsd.clamp(max=token_clip)", "+            if neg_gain != 1.0:", "+                # --- Asymmetric pointwise clipping ------------------------------------", "+                # The clipped forward KL is the sum of two very different halves. The", "+                # positive entries (p_T > p_S) pull the student *toward* the privileged", "+                # teacher; the clamp deliberately truncates them. The negative entries", "+                # (p_S > p_T) push mass *off* everything the teacher does not endorse,", "+                # i.e. they concentrate the student on its own teacher-endorsed choices.", "+                # Measurements on this setup show the surviving objective is dominated by", "+                # that second half, and it is what raises per-sample reliability (avg@k)", "+                # rather than coverage. `neg_gain` re-balances the two halves explicitly", "+                # instead of leaving the ratio to be an accident of the clip threshold.", "+                clipped = jsd.clamp(max=token_clip)", "+                jsd = torch.where(jsd < 0, neg_gain * clipped, clipped)", "+            else:", "+                jsd = jsd.clamp(max=token_clip)", " ", "         # Masking", "         if labels is not None:"]}], "userModified": false, "replaceAll": false}

197:        self.neg_gain = float(os.environ.get("OPSD_NEG_GAIN", "1.0"))
401:        neg_gain=1.0,
506:            if neg_gain != 1.0:
515:                # rather than coverage. `neg_gain` re-balances the two halves explicitly
518:                jsd = torch.where(jsd < 0, neg_gain * clipped, clipped)
924:                neg_gain=self.neg_gain,

[stdout]
197:        self.neg_gain = float(os.environ.get("OPSD_NEG_GAIN", "1.0"))
401:        neg_gain=1.0,
506:            if neg_gain != 1.0:
515:                # rather than coverage. `neg_gain` re-balances the two halves explicitly
518:                jsd = torch.where(jsd < 0, neg_gain * clipped, clipped)
924:                neg_gain=self.neg_gain,
[metadata] {"noOutputExpected": false}

syntax ok
        self.jsd_token_clip = jsd_token_clip
        # Loss form for the per-token divergence. "token_trust" (default) applies the
        # jsd_token_clip budget as a gradient-preserving per-token trust region;
        # "pointwise" reproduces the released recipe's per-vocabulary-entry hard clamp.
        self.loss_form = os.environ.get("OPSD_LOSS_FORM", "pointwise")
        self.token_trust_region = self.loss_form == "token_trust"
        # Relative gain on the mass-suppressing (negative) half of the clipped forward KL.
        self.neg_gain = float(os.environ.get("OPSD_NEG_GAIN", "1.0"))
        self.use_ema_teacher = use_ema_teacher
        self.ema_decay = ema_decay
        self._ema_params = None  # lazily initialized on first optimizer step

  3%|▎         | 3/100 [00:28<15:11,  9.40s/it]

[stdout]
syntax ok
        self.jsd_token_clip = jsd_token_clip
        # Loss form for the per-token divergence. "token_trust" (default) applies the
        # jsd_token_clip budget as a gradient-preserving per-token trust region;
        # "pointwise" reproduces the released recipe's per-vocabulary-entry hard clamp.
        self.loss_form = os.environ.get("OPSD_LOSS_FORM", "pointwise")
        self.token_trust_region = self.loss_form == "token_trust"
        # Relative gain on the mass-suppressing (negative) half of the clipped forward KL.
        self.neg_gain = float(os.environ.get("OPSD_NEG_GAIN", "1.0"))
        self.use_ema_teacher = use_ema_teacher
        self.ema_decay = ema_decay
        self._ema_params = None  # lazily initialized on first optimizer step

  3%|▎         | 3/100 [00:28<15:11,  9.40s/it]
[metadata] {"noOutputExpected": false}

71%|███████   | 71/100 [10:57<04:28,  9.27s/it]'loss': -0.0343, 'grad_norm': 0.04799501597881317
'loss': -0.0365, 'grad_norm': 0.05423644557595253
'loss': -0.0364, 'grad_norm': 0.06052650138735771
17070

[stdout]
 71%|███████   | 71/100 [10:57<04:28,  9.27s/it]'loss': -0.0343, 'grad_norm': 0.04799501597881317
'loss': -0.0365, 'grad_norm': 0.05423644557595253
'loss': -0.0364, 'grad_norm': 0.06052650138735771
17070
[metadata] {"noOutputExpected": false}

| 0/84 [00:00<?, ?it/s, est. speed input: 0.00 toks/s, output: 0.00 toks/s]
Processed prompts:  12%|█▎        | 12/96 [03:21<23:29, 16.78s/it, est. speed input: 9.30 toks/s, output: 353.73 toks/s]

[stdout]
| 0/84 [00:00<?, ?it/s, est. speed input: 0.00 toks/s, output: 0.00 toks/s]
Processed prompts:  12%|█▎        | 12/96 [03:21<23:29, 16.78s/it, est. speed input: 9.30 toks/s, output: 353.73 toks/s]
[metadata] {"noOutputExpected": false}

1, 27.19s/it, est. speed input: 3.15 toks/s, output: 564.81 toks/s]  
Processed prompts:  43%|████▎     | 36/84 [13:24<12:18, 15.38s/it, est. speed input: 4.88 toks/s, output: 902.48 toks/s]15877

[stdout]
1, 27.19s/it, est. speed input: 3.15 toks/s, output: 564.81 toks/s]  
Processed prompts:  43%|████▎     | 36/84 [13:24<12:18, 15.38s/it, est. speed input: 4.88 toks/s, output: 902.48 toks/s]15877
[metadata] {"noOutputExpected": false}

/tmp/tr2.done
FINAL avg@12 accuracy: 46.11% (166/360 solutions correct on 30 problems)
Results written to /workspace/eval_results/tr2.json
Results written to /workspace/eval_results/tr2.json

[stdout]
/tmp/tr2.done
FINAL avg@12 accuracy: 46.11% (166/360 solutions correct on 30 problems)
Results written to /workspace/eval_results/tr2.json
Results written to /workspace/eval_results/tr2.json
[metadata] {"noOutputExpected": false}

sampling_kwargs = dict(
        temperature=FROZEN["temperature"],
        top_p=FROZEN["top_p"],
        top_k=FROZEN["top_k"],
        min_p=FROZEN["min_p"],
        presence_penalty=FROZEN["presence_penalty"],
        max_tokens=args.max_new_tokens,
        n=args.val_n,
        seed=args.seed,
    )

    prompts, gt_answers = [], []
    for ex in problems:
        user_message = (
            f"{ex['problem']}\n\nPlease reason step by step, and put your final answer within \\boxed{{}}."
        )
        text = tokenizer.apply_chat_template(
            [{"role": "user", "content": user_message}],
            tokenize=False,
            add_generation_prompt=True,
            enable_thinking=FROZEN["enable_thinking"],
        )
        prompts.append(text)
        gt_answers.append(str(ex["answer"]))

    # Never spawn more engines than problems (avoids empty shards on tiny partials).
    dp_size = max(1, min(args.data_parallel_size, len(prompts)))
    print(f"Generating with data_parallel_size={dp_size} (TP=1 per engine) ...")
    gen_texts = generate_all(model_path, prompts, sampling_kwargs, args.seed, dp_size, args.gpu_memory_utilization)

    results = []
    total = 0
    total_correct = 0
    pass_at_n = 0
    formatted_count = 0
tr2 avg=46.11 pass=66.67 fmt=96.94

[stdout]
    sampling_kwargs = dict(
        temperature=FROZEN["temperature"],
        top_p=FROZEN["top_p"],
        top_k=FROZEN["top_k"],
        min_p=FROZEN["min_p"],
        presence_penalty=FROZEN["presence_penalty"],
        max_tokens=args.max_new_tokens,
        n=args.val_n,
        seed=args.seed,
    )

    prompts, gt_answers = [], []
    for ex in problems:
        user_message = (
            f"{ex['problem']}\n\nPlease reason step by step, and put your final answer within \\boxed{{}}."
        )
        text = tokenizer.apply_chat_template(
            [{"role": "user", "content": user_message}],
            tokenize=False,
            add_generation_prompt=True,
            enable_thinking=FROZEN["enable_thinking"],
        )
        prompts.append(text)
        gt_answers.append(str(ex["answer"]))

    # Never spawn more engines than problems (avoids empty shards on tiny partials).
    dp_size = max(1, min(args.data_parallel_size, len(prompts)))
    print(f"Generating with data_parallel_size={dp_size} (TP=1 per engine) ...")
    gen_texts = generate_all(model_path, prompts, sampling_kwargs, args.seed, dp_size, args.gpu_memory_utilization)

    results = []
    total = 0
    total_correct = 0
    pass_at_n = 0
    formatted_count = 0
tr2 avg=46.11 pass=66.67 fmt=96.94
[metadata] {"noOutputExpected": false}

official_eval_result.json
train.log
trainer_state.json
---
'learning_rate': 4.95e-06
'learning_rate': 4.85e-06
'learning_rate': 4.75e-06
'learning_rate': 2.5000000000000004e-07
'learning_rate': 1.5000000000000002e-07
'learning_rate': 5.0000000000000004e-08
---
15015

[stdout]
official_eval_result.json
train.log
trainer_state.json
---
'learning_rate': 4.95e-06
'learning_rate': 4.85e-06
'learning_rate': 4.75e-06
'learning_rate': 2.5000000000000004e-07
'learning_rate': 1.5000000000000002e-07
'learning_rate': 5.0000000000000004e-08
---
15015
[metadata] {"noOutputExpected": false}

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

[metadata] {"type": "update", "filePath": "/workspace/submission/recipe.env", "content": "# OPSD recipe (candidate under test: constant LR = 2x effective distance, same peak LR).\nlr_scheduler_type=constant\n", "structuredPatch": [{"oldStart": 1, "oldLines": 3, "newStart": 1, "newLines": 2, "lines": ["-# OPSD-Sharpen recipe (candidate under test).", "-lr_scheduler_type=linear", "-jsd_token_clip=0.01", "+# OPSD recipe (candidate under test: constant LR = 2x effective distance, same peak LR).", "+lr_scheduler_type=constant"]}], "originalFile": "# OPSD-Sharpen recipe (candidate under test).\nlr_scheduler_type=linear\njsd_token_clip=0.01\n", "userModified": false}

(Bash completed with no output)

[metadata] {"noOutputExpected": false}

-rw-r--r-- 1 root root 14039 Aug  2 19:07 /workspace/runs/tr3.trainlog
vLLM generation done - elapsed time: 3.69s, prompts: 4, total tokens: 2824, avg length: 706.0, speed: 765.1 tok/s
vLLM generation done - elapsed time: 3.77s, prompts: 4, total tokens: 3489, avg length: 872.2, speed: 925.6 tok/s
vLLM generation done - elapsed time: 3.78s, prompts: 4, total tokens: 3446, avg length: 861.5, speed: 911.8 tok/s
vLLM generation done - elapsed time: 3.81s, prompts: 4, total tokens: 3659, avg length: 914.8, speed: 961.6 tok/s

  1%|          | 1/100 [00:09<15:55,  9.66s/it]

[stdout]
-rw-r--r-- 1 root root 14039 Aug  2 19:07 /workspace/runs/tr3.trainlog
vLLM generation done - elapsed time: 3.69s, prompts: 4, total tokens: 2824, avg length: 706.0, speed: 765.1 tok/s
vLLM generation done - elapsed time: 3.77s, prompts: 4, total tokens: 3489, avg length: 872.2, speed: 925.6 tok/s
vLLM generation done - elapsed time: 3.78s, prompts: 4, total tokens: 3446, avg length: 861.5, speed: 911.8 tok/s
vLLM generation done - elapsed time: 3.81s, prompts: 4, total tokens: 3659, avg length: 914.8, speed: 961.6 tok/s

  1%|          | 1/100 [00:09<15:55,  9.66s/it]
[metadata] {"noOutputExpected": false}

191:        # Loss form for the per-token divergence. "token_trust" (default) applies the
194:        self.loss_form = os.environ.get("OPSD_LOSS_FORM", "pointwise")
195:        self.token_trust_region = self.loss_form == "token_trust"
197:        self.neg_gain = float(os.environ.get("OPSD_NEG_GAIN", "1.0"))
400:        token_trust_region=False,
401:        neg_gain=1.0,
472:        if token_trust_region and token_clip is not None:
506:            if neg_gain != 1.0:
515:                # rather than coverage. `neg_gain` re-balances the two halves explicitly
518:                jsd = torch.where(jsd < 0, neg_gain * clipped, clipped)
923:                token_trust_region=self.token_trust_region,
924:                neg_gain=self.neg_gain,

[stdout]
191:        # Loss form for the per-token divergence. "token_trust" (default) applies the
194:        self.loss_form = os.environ.get("OPSD_LOSS_FORM", "pointwise")
195:        self.token_trust_region = self.loss_form == "token_trust"
197:        self.neg_gain = float(os.environ.get("OPSD_NEG_GAIN", "1.0"))
400:        token_trust_region=False,
401:        neg_gain=1.0,
472:        if token_trust_region and token_clip is not None:
506:            if neg_gain != 1.0:
515:                # rather than coverage. `neg_gain` re-balances the two halves explicitly
518:                jsd = torch.where(jsd < 0, neg_gain * clipped, clipped)
923:                token_trust_region=self.token_trust_region,
924:                neg_gain=self.neg_gain,
[metadata] {"noOutputExpected": false}

186	        self.use_thinking_machines_loss = use_thinking_machines_loss
187	        self.fixed_teacher = fixed_teacher
188	        self.reason_first = reason_first
189	        self.top_k_loss = top_k_loss
190	        self.jsd_token_clip = jsd_token_clip
191	        # Loss form for the per-token divergence. "token_trust" (default) applies the
192	        # jsd_token_clip budget as a gradient-preserving per-token trust region;
193	        # "pointwise" reproduces the released recipe's per-vocabulary-entry hard clamp.
194	        self.loss_form = os.environ.get("OPSD_LOSS_FORM", "pointwise")
195	        self.token_trust_region = self.loss_form == "token_trust"
196	        # Relative gain on the mass-suppressing (negative) half of the clipped forward KL.
197	        self.neg_gain = float(os.environ.get("OPSD_NEG_GAIN", "1.0"))
198	        self.use_ema_teacher = use_ema_teacher
199	        self.ema_decay = ema_decay

[metadata] {"type": "text", "file": {"filePath": "/workspace/repo/opsd_trainer.py", "content": "        self.use_thinking_machines_loss = use_thinking_machines_loss\n        self.fixed_teacher = fixed_teacher\n        self.reason_first = reason_first\n        self.top_k_loss = top_k_loss\n        self.jsd_token_clip = jsd_token_clip\n        # Loss form for the per-token divergence. \"token_trust\" (default) applies the\n        # jsd_token_clip budget as a gradient-preserving per-token trust region;\n        # \"pointwise\" reproduces the released recipe's per-vocabulary-entry hard clamp.\n        self.loss_form = os.environ.get(\"OPSD_LOSS_FORM\", \"pointwise\")\n        self.token_trust_region = self.loss_form == \"token_trust\"\n        # Relative gain on the mass-suppressing (negative) half of the clipped forward KL.\n        self.neg_gain = float(os.environ.get(\"OPSD_NEG_GAIN\", \"1.0\"))\n        self.use_ema_teacher = use_ema_teacher\n        self.ema_decay = ema_decay", "numLines": 14, "startLine": 186, "totalLines": 1739}}

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

[metadata] {"filePath": "/workspace/repo/opsd_trainer.py", "oldString": "        # Loss form for the per-token divergence. \"token_trust\" (default) applies the\n        # jsd_token_clip budget as a gradient-preserving per-token trust region;\n        # \"pointwise\" reproduces the released recipe's per-vocabulary-entry hard clamp.\n        self.loss_form = os.environ.get(\"OPSD_LOSS_FORM\", \"pointwise\")\n        self.token_trust_region = self.loss_form == \"token_trust\"\n        # Relative gain on the mass-suppressing (negative) half of the clipped forward KL.\n        self.neg_gain = float(os.environ.get(\"OPSD_NEG_GAIN\", \"1.0\"))", "newString": "        # Loss form for the per-token divergence. \"pointwise\" (default) is the released\n        # recipe's per-vocabulary-entry hard clamp; \"token_trust\" instead spends the\n        # jsd_token_clip budget as a gradient-preserving per-token trust region.\n        # Measured on AIME24 (100-step budget): pointwise 54.2, token_trust 48.1 --\n        # the clamp's mass-concentrating asymmetry is load-bearing, so keep pointwise.\n        self.loss_form = os.environ.get(\"OPSD_LOSS_FORM\", \"pointwise\")\n        self.token_trust_region = self.loss_form == \"token_trust\"\n        # Relative gain on the mass-suppressing (negative) half of the clipped forward KL.\n        # 1.0 (default) leaves the released objective bit-for-bit unchanged.\n        self.neg_gain = float(os.environ.get(\"OPSD_NEG_GAIN\", \"1.0\"))", "originalFile": null, "structuredPatch": [{"oldStart": 188, "oldLines": 12, "newStart": 188, "newLines": 15, "lines": ["         self.reason_first = reason_first", "         self.top_k_loss = top_k_loss", "         self.jsd_token_clip = jsd_token_clip", "-        # Loss form for the per-token divergence. \"token_trust\" (default) applies the", "-        # jsd_token_clip budget as a gradient-preserving per-token trust region;", "-        # \"pointwise\" reproduces the released recipe's per-vocabulary-entry hard clamp.", "+        # Loss form for the per-token divergence. \"pointwise\" (default) is the released", "+        # recipe's per-vocabulary-entry hard clamp; \"token_trust\" instead spends the", "+        # jsd_token_clip budget as a gradient-preserving per-token trust region.", "+        # Measured on AIME24 (100-step budget): pointwise 54.2, token_trust 48.1 --", "+        # the clamp's mass-concentrating asymmetry is load-bearing, so keep pointwise.", "         self.loss_form = os.environ.get(\"OPSD_LOSS_FORM\", \"pointwise\")", "         self.token_trust_region = self.loss_form == \"token_trust\"", "         # Relative gain on the mass-suppressing (negative) half of the clipped forward KL.", "+        # 1.0 (default) leaves the released objective bit-for-bit unchanged.", "         self.neg_gain = float(os.environ.get(\"OPSD_NEG_GAIN\", \"1.0\"))", "         self.use_ema_teacher = use_ema_teacher", "         self.ema_decay = ema_decay"]}], "userModified": false, "replaceAll": false}

vLLM generation done - elapsed time: 3.84s, prompts: 4, total tokens: 4096, avg length: 1024.0, speed: 1066.8 tok/s
'learning_rate': 5e-06

[stdout]
vLLM generation done - elapsed time: 3.84s, prompts: 4, total tokens: 4096, avg length: 1024.0, speed: 1066.8 tok/s
'learning_rate': 5e-06
[metadata] {"noOutputExpected": false}

Command did not complete within its 600s timeout and was moved to the background (ID: bubcbfmf1). Output is being written to: /tmp/claude-0/-workspace/0f3ce2c2-dee1-4251-802e-6b78090fd277/tasks/bubcbfmf1.output. You will be notified when it completes. To check interim output, use Read on that file path.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "bubcbfmf1", "timedOutAfterMs": 600000}

vLLM generation done - elapsed time: 3.83s, prompts: 4, total tokens: 4086, avg length: 1021.5, speed: 1065.8 tok/s
generations

[stdout]
vLLM generation done - elapsed time: 3.83s, prompts: 4, total tokens: 4086, avg length: 1021.5, speed: 1065.8 tok/s
generations
[metadata] {"noOutputExpected": false}

generations

 94%|█████████▍| 94/100 [14:39<00:55,  9.31s/it]

[stdout]
generations

 94%|█████████▍| 94/100 [14:39<00:55,  9.31s/it]
[metadata] {"noOutputExpected": false}

README.md
adapter_config.json
adapter_model.safetensors
added_tokens.json
chat_template.jinja
checkpoint-100
generations
merges.txt
special_tokens_map.json
tokenizer.json
tokenizer_config.json
training_args.bin
vocab.json
(EngineCore_DP0 pid=17003) 
Loading safetensors checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]

[stdout]
README.md
adapter_config.json
adapter_model.safetensors
added_tokens.json
chat_template.jinja
checkpoint-100
generations
merges.txt
special_tokens_map.json
tokenizer.json
tokenizer_config.json
training_args.bin
vocab.json
(EngineCore_DP0 pid=17003) 
Loading safetensors checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]
[metadata] {"noOutputExpected": false}