claude-opus-5

6:419-        temperature=1.0,
15:428-        Compute the generalized Jensen-Shannon Divergence loss for knowledge distillation using F.kl_div. See Eq. (1)
28:441-            temperature:
29:442-                Softmax temperature (default: 1.0)

[stdout]
6:419-        temperature=1.0,
15:428-        Compute the generalized Jensen-Shannon Divergence loss for knowledge distillation using F.kl_div. See Eq. (1)
28:441-            temperature:
29:442-                Softmax temperature (default: 1.0)
[metadata] {"noOutputExpected": false}

prefers (the gradient on that logit becomes +q_j * P_unclipped > 0).
                * `"token"` (default) — bound the *influence of each token* instead, keeping the
                  gradient direction intact: with k_t the token's full divergence, the loss uses
                  w_t * k_t with a detached w_t = min(1, token_clip / k_t). Tokens whose
                  divergence is below the threshold are untouched; above it their loss saturates
                  at `token_clip` exactly as before, but the gradient stays proportional to the
                  true divergence gradient (q - p), merely down-weighted. Same stabilising effect,
                  no sign reversal, and no discarding of the teaching signal.
            stats:
                Optional dict; filled in with diagnostics (mean/saturation of the per-token divergence).

        Returns:
            loss: Scalar tensor with the generalized JSD loss
        """

        if logits_are_probs:
            student_log_probs = torch.log(student_logits.clamp_min(1e-8))
            teacher_log_probs = torch.log(teacher_logits.clamp_min(1e-8))
        else:
            # Apply temperature scaling to logits before computing probabilities
            student_logits = student_logits / temperature
            teacher_logits = teacher_logits / temperature

            if top_k is not None and top_k > 0:
                # Restrict to top-k tokens of the teacher distribution and renormalize.
                # Shape: [batch, seq_len, top_k]
                _, top_k_indices = torch.topk(teacher_logits, k=top_k, dim=-1)
                student_logits = torch.gather(student_logits, dim=-1, index=top_k_indices)
                teacher_logits = torch.gather(teacher_logits, dim=-1, index=top_k_indices)

            # Compute log probabilities for student and probabilities for teacher
            student_log_probs = F.log_softmax(student_logits, dim=-1)
            teacher_log_probs = F.log_softmax(teacher_logits, dim=-1)

        if beta == 0:
            jsd = F.kl_div(student_log_probs, teacher_log_probs, reduction="none", log_target=True)
        elif beta == 1:
            jsd = F.kl_div(teacher_log_probs, student_log_probs, reduction="none", log_target=True)
        else:
            # Compute the log of the mixture distribution
            # log(a + b) = log(exp(log(a)) + exp(log(b))) -> for mixture
            beta = torch.tensor(beta, dtype=student_log_probs.dtype, device=student_log_probs.device)
            mixture_log_probs = torch.logsumexp(
                torch.stack([student_log_probs + torch.log1p(-beta), teacher_log_probs + torch.log(beta)]),
                dim=0,
            )

            # Compute KL divergences using F.kl_div
            # PyTorch differs from the standard mathematical definition, so the order of the probability distributions is swapped compared to that defined in the paper.
            kl_teacher = F.kl_div(mixture_log_probs, teacher_log_probs, reduction="none", log_target=True)
            kl_student = F.kl_div(mixture_log_probs, student_log_probs, reduction="none", log_target=True)

            # Compute the Generalized Jensen-Shannon Divergence
            jsd = beta * kl_teacher + (1 - beta) * kl_student

        # ---- influence bounding (see `clip_mode` in the docstring) ----
        if token_clip is not None and clip_mode == "token":
            # Per-token divergence, then a *detached* down-weight so that the loss saturates at
            # `token_clip` while the gradient keeps pointing along the true divergence gradient.
            per_token = jsd.sum(dim=-1)  # [batch, seq_len]
            weight = (token_clip / per_token.detach().clamp_min(1e-8)).clamp(max=1.0)
            jsd = weight * per_token
            if labels is not None:
                mask = labels != -100
                jsd = jsd[mask]
                if stats is not None and mask.any():
                    with torch.no_grad():
                        k = per_token[mask].detach().float()
                        stats["div_mean"] = k.mean()
                        stats["div_saturated"] = (k > token_clip).float().mean()
            if reduction in ("batchmean", "mean"):

[stdout]
                  prefers (the gradient on that logit becomes +q_j * P_unclipped > 0).
                * `"token"` (default) — bound the *influence of each token* instead, keeping the
                  gradient direction intact: with k_t the token's full divergence, the loss uses
                  w_t * k_t with a detached w_t = min(1, token_clip / k_t). Tokens whose
                  divergence is below the threshold are untouched; above it their loss saturates
                  at `token_clip` exactly as before, but the gradient stays proportional to the
                  true divergence gradient (q - p), merely down-weighted. Same stabilising effect,
                  no sign reversal, and no discarding of the teaching signal.
            stats:
                Optional dict; filled in with diagnostics (mean/saturation of the per-token divergence).

        Returns:
            loss: Scalar tensor with the generalized JSD loss
        """

        if logits_are_probs:
            student_log_probs = torch.log(student_logits.clamp_min(1e-8))
            teacher_log_probs = torch.log(teacher_logits.clamp_min(1e-8))
        else:
            # Apply temperature scaling to logits before computing probabilities
            student_logits = student_logits / temperature
            teacher_logits = teacher_logits / temperature

            if top_k is not None and top_k > 0:
                # Restrict to top-k tokens of the teacher distribution and renormalize.
                # Shape: [batch, seq_len, top_k]
                _, top_k_indices = torch.topk(teacher_logits, k=top_k, dim=-1)
                student_logits = torch.gather(student_logits, dim=-1, index=top_k_indices)
                teacher_logits = torch.gather(teacher_logits, dim=-1, index=top_k_indices)

            # Compute log probabilities for student and probabilities for teacher
            student_log_probs = F.log_softmax(student_logits, dim=-1)
            teacher_log_probs = F.log_softmax(teacher_logits, dim=-1)

        if beta == 0:
            jsd = F.kl_div(student_log_probs, teacher_log_probs, reduction="none", log_target=True)
        elif beta == 1:
            jsd = F.kl_div(teacher_log_probs, student_log_probs, reduction="none", log_target=True)
        else:
            # Compute the log of the mixture distribution
            # log(a + b) = log(exp(log(a)) + exp(log(b))) -> for mixture
            beta = torch.tensor(beta, dtype=student_log_probs.dtype, device=student_log_probs.device)
            mixture_log_probs = torch.logsumexp(
                torch.stack([student_log_probs + torch.log1p(-beta), teacher_log_probs + torch.log(beta)]),
                dim=0,
            )

            # Compute KL divergences using F.kl_div
            # PyTorch differs from the standard mathematical definition, so the order of the probability distributions is swapped compared to that defined in the paper.
            kl_teacher = F.kl_div(mixture_log_probs, teacher_log_probs, reduction="none", log_target=True)
            kl_student = F.kl_div(mixture_log_probs, student_log_probs, reduction="none", log_target=True)

            # Compute the Generalized Jensen-Shannon Divergence
            jsd = beta * kl_teacher + (1 - beta) * kl_student

        # ---- influence bounding (see `clip_mode` in the docstring) ----
        if token_clip is not None and clip_mode == "token":
            # Per-token divergence, then a *detached* down-weight so that the loss saturates at
            # `token_clip` while the gradient keeps pointing along the true divergence gradient.
            per_token = jsd.sum(dim=-1)  # [batch, seq_len]
            weight = (token_clip / per_token.detach().clamp_min(1e-8)).clamp(max=1.0)
            jsd = weight * per_token
            if labels is not None:
                mask = labels != -100
                jsd = jsd[mask]
                if stats is not None and mask.any():
                    with torch.no_grad():
                        k = per_token[mask].detach().float()
                        stats["div_mean"] = k.mean()
                        stats["div_saturated"] = (k > token_clip).float().mean()
            if reduction in ("batchmean", "mean"):
[metadata] {"noOutputExpected": false}

=== [e2] train rc=0 05:12:42
/workspace/runs/e2/capped_seed42/checkpoint-100
=== [e2] eval start 05:12:42
15360

[stdout]
=== [e2] train rc=0 05:12:42
/workspace/runs/e2/capped_seed42/checkpoint-100
=== [e2] eval start 05:12:42
15360
[metadata] {"noOutputExpected": false}

=== [e2] eval start 05:12:42
=== [e2] eval rc=0 05:34:53
RESULT e2: avg@12=53.33 pass@12=80.00 fmt=99.7
=== [e2] done 05:34:53 (remaining 15199s)
14809

[stdout]
=== [e2] eval start 05:12:42
=== [e2] eval rc=0 05:34:53
RESULT e2: avg@12=53.33 pass@12=80.00 fmt=99.7
=== [e2] done 05:34:53 (remaining 15199s)
14809
[metadata] {"noOutputExpected": false}

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 04:03:25 2026).
['source', 'problem', 'solution', 'messages', 'system', 'conversations', 'generated_token_count', 'correct', 'Question', 'COT_Reason', 'Answer'] 29434
short-numeric: 14093 / 29434
tok count pct: [1408. 2853. 4496.] mean 2897.0
numeric & tc>=2000: 9775
first 3 answers: ['\\frac{3\\sqrt{3}}{2}', 'D', '72']

[stdout]
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 04:03:25 2026).
['source', 'problem', 'solution', 'messages', 'system', 'conversations', 'generated_token_count', 'correct', 'Question', 'COT_Reason', 'Answer'] 29434
short-numeric: 14093 / 29434
tok count pct: [1408. 2853. 4496.] mean 2897.0
numeric & tc>=2000: 9775
first 3 answers: ['\\frac{3\\sqrt{3}}{2}', 'D', '72']
[metadata] {"noOutputExpected": false}

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 04:03:25 2026).
correct: Counter({True: 29434})
source: [('olympiads', 21315), ('math', 5351), ('aops_forum', 2291), ('amc_aime', 477)]
numeric&correct: 14093  numeric&correct&tc>=2000: 9775
sol vs COT_Reason lens: 3200 10084
solution==COT_Reason? 0

[stdout]
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 04:03:25 2026).
correct: Counter({True: 29434})
source: [('olympiads', 21315), ('math', 5351), ('aops_forum', 2291), ('amc_aime', 477)]
numeric&correct: 14093  numeric&correct&tc>=2000: 9775
sol vs COT_Reason lens: 3200 10084
solution==COT_Reason? 0
[metadata] {"noOutputExpected": false}

9:    Teacher: sees problem + solution + transition prompt (with chat template)
29:        # Prompt for reasoning about the solution before teaching
32:            "Please analyze this solution and explain the key reasoning steps and problem-solving strategies employed. "
33:            "Do NOT use <think> tags. Do NOT derive your own solution. "
34:            "Simply analyze and explain the reference solution provided above.\n"
38:            "\n\nAfter reading the reference solution above, make sure you truly understand "
61:            # Extract problem and solution from dataset
64:            solution = feature["solution"]
77:                # Reasoning prompt: ask teacher to analyze the solution
82:                    f"{solution}\n"
99:                    f"Here is a reference solution to this problem:\n"
100:                    f"=== Reference Solution Begin ===\n{solution}\n=== Reference Solution End ===\n"

[stdout]
9:    Teacher: sees problem + solution + transition prompt (with chat template)
29:        # Prompt for reasoning about the solution before teaching
32:            "Please analyze this solution and explain the key reasoning steps and problem-solving strategies employed. "
33:            "Do NOT use <think> tags. Do NOT derive your own solution. "
34:            "Simply analyze and explain the reference solution provided above.\n"
38:            "\n\nAfter reading the reference solution above, make sure you truly understand "
61:            # Extract problem and solution from dataset
64:            solution = feature["solution"]
77:                # Reasoning prompt: ask teacher to analyze the solution
82:                    f"{solution}\n"
99:                    f"Here is a reference solution to this problem:\n"
100:                    f"=== Reference Solution Begin ===\n{solution}\n=== Reference Solution End ===\n"
[metadata] {"noOutputExpected": false}

We know that:
   \[
   F'(x) = 4x^2 + 9x^{-2}
   \]
   
   To find \( F(x) \), we need to integrate \( F'(x) \):
   \[
   F(x) = \int (4x^2 + 9x^{-2}) \, dx
   \]

2. **Perform the integration**:
   
   The integral can be split into two separate integrals:
   \[
   F(x) = \int 4x^2 \, dx + \int 9x^{-2} \, dx
   \]

   Compute each integral separately:
   \[
   \int 4x^2 \, dx = 4 \int x^2 \, dx = 4 \cdot \frac{x^3}{3} = \frac{4x^3}{3}
   \]
   \[
   \int 9x^{-2} \, dx = 9 \int x^{-2} \, dx = 9 \cdot \l
--- COT_Reason (len 3475) ---
Okay, so I need to find the function F(x) whose graph passes through the point M₀(3, -2), and we know that the derivative F'(x) is 4x² + 9x⁻². Hmm, let's start by recalling that to find F(x), we need to integrate F'(x) with respect to x. Then, we can use the point M₀ to determine the constant of integration. Let me break this down step by step.

First, let's write down the derivative again to make sure I have it right: F'(x) = 4x² + 9x⁻². So, integrating this should give me F(x) plus a constant. The integral of F'(x) is F(x) + C, where C is the constant. But since we have a specific point that the graph passes through, once we integrate, we can plug in x = 3 and set F(3) = -2 to solve for C.
--- Answer --- F(x)=\frac{4}{3} x^{3}-\frac{9}{x}-35
solution len med 1684.0  cot len med 7044.0

[stdout]
   We know that:
   \[
   F'(x) = 4x^2 + 9x^{-2}
   \]
   
   To find \( F(x) \), we need to integrate \( F'(x) \):
   \[
   F(x) = \int (4x^2 + 9x^{-2}) \, dx
   \]

2. **Perform the integration**:
   
   The integral can be split into two separate integrals:
   \[
   F(x) = \int 4x^2 \, dx + \int 9x^{-2} \, dx
   \]

   Compute each integral separately:
   \[
   \int 4x^2 \, dx = 4 \int x^2 \, dx = 4 \cdot \frac{x^3}{3} = \frac{4x^3}{3}
   \]
   \[
   \int 9x^{-2} \, dx = 9 \int x^{-2} \, dx = 9 \cdot \l
--- COT_Reason (len 3475) ---
Okay, so I need to find the function F(x) whose graph passes through the point M₀(3, -2), and we know that the derivative F'(x) is 4x² + 9x⁻². Hmm, let's start by recalling that to find F(x), we need to integrate F'(x) with respect to x. Then, we can use the point M₀ to determine the constant of integration. Let me break this down step by step.

First, let's write down the derivative again to make sure I have it right: F'(x) = 4x² + 9x⁻². So, integrating this should give me F(x) plus a constant. The integral of F'(x) is F(x) + C, where C is the constant. But since we have a specific point that the graph passes through, once we integrate, we can plug in x = 3 and set F(3) = -2 to solve for C.
--- Answer --- F(x)=\frac{4}{3} x^{3}-\frac{9}{x}-35
solution len med 1684.0  cot len med 7044.0
[metadata] {"noOutputExpected": false}

===
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 04:03:25 2026).
"so x cannot be zero. However, the original function F(x) also has a term -9/x, so x cannot be zero. The graph passes through (3, -2), so x=3 is in the domain, which is fine. So the function is defined for x ≠ 0, but since the point is at x=3, that's okay.\n\nTherefore, the final answer is F(x) = (4/3)x³ - 9/x - 35.\n\n**Final Answer**\nThe function is \\boxed{F(x) = \\dfrac{4}{3}x^3 - \\dfrac{9}{x} - 35}."

[stdout]
===
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 04:03:25 2026).
"so x cannot be zero. However, the original function F(x) also has a term -9/x, so x cannot be zero. The graph passes through (3, -2), so x=3 is in the domain, which is fine. So the function is defined for x ≠ 0, but since the point is at x=3, that's okay.\n\nTherefore, the final answer is F(x) = (4/3)x³ - 9/x - 35.\n\n**Final Answer**\nThe function is \\boxed{F(x) = \\dfrac{4}{3}x^3 - \\dfrac{9}{x} - 35}."
[metadata] {"noOutputExpected": false}

1	import torch
2	
3	
4	class SelfDistillationDataCollator:
5	    """
6	    Data collator for self-distillation that creates both student and teacher inputs.
7	
8	    Student: sees only the problem (with chat template)
9	    Teacher: sees problem + solution + transition prompt (with chat template)
10	
11	    To enable batch-level operations (like original GKD), we pad prompts to the same length
12	    within each batch, and track the actual (unpadded) prompt lengths for loss masking.
13	    """
14	
15	    def __init__(
16	        self,
17	        tokenizer,
18	        max_length=2048,
19	        reason_first=True,
20	        student_thinking=False,
21	        teacher_thinking=True,
22	    ):
23	        self.tokenizer = tokenizer
24	        self.max_length = max_length
25	        self.reason_first = reason_first
26	        self.student_thinking = student_thinking
27	        self.teacher_thinking = teacher_thinking
28	
29	        # Prompt for reasoning about the solution before teaching
30	        self.reason_first_prompt = (
31	            "\n\nThe reference reasoning above arrives at the correct answer. "
32	            "Please analyze this solution and explain the key reasoning steps and problem-solving strategies employed. "
33	            "Do NOT use <think> tags. Do NOT derive your own solution. "
34	            "Simply analyze and explain the reference solution provided above.\n"
35	        )
36	        # Prompt for transitioning to teaching mode after reasoning
37	        self.transition_prompt = (
38	            "\n\nAfter reading the reference solution above, make sure you truly understand "
39	            "the reasoning behind each step — do not copy or paraphrase it. Now, using your "
40	            "own words and independent reasoning, derive the same final answer to the problem above. "
41	            "Think step by step, explore different approaches, and don't be afraid to backtrack "
42	            "or reconsider if something doesn't work out:\n"
43	        )
44	
45	        # Set padding side explicitly for consistency
46	        print(f"[DataCollator] Original padding_side: {self.tokenizer.padding_side}")
47	        self.tokenizer.padding_side = "right"
48	        print(f"[DataCollator] Set padding_side to: {self.tokenizer.padding_side}")
49	        print(f"[DataCollator] Reason first mode: {self.reason_first}")
50	
51	    def __call__(self, features):
52	
53	        batch_size = len(features)
54	
55	        # Prepare student and teacher prompts using chat template (matching evaluation)
56	        student_prompts = []
57	        teacher_prompts = []
58	        teacher_reasoning_prompts = []  # NEW: for reason_first mode
59	
60	        for feature in features:
61	            # Extract problem and solution from dataset
62	            # Handle different possible column names
63	            problem = feature["problem"]
64	            solution = feature["solution"]
65	
66	            # Student prompt: just the problem with instruction (matching evaluation format)
67	            student_user_message = f"Problem: {problem}\n\nPlease reason step by step, and put your final answer within \\boxed{{}}."
68	            student_messages = [{"role": "user", "content": student_user_message}]
69	
70	            # Apply chat template for student (matching evaluation)

[metadata] {"type": "text", "file": {"filePath": "/workspace/repo/data_collator.py", "content": "import torch\n\n\nclass SelfDistillationDataCollator:\n    \"\"\"\n    Data collator for self-distillation that creates both student and teacher inputs.\n\n    Student: sees only the problem (with chat template)\n    Teacher: sees problem + solution + transition prompt (with chat template)\n\n    To enable batch-level operations (like original GKD), we pad prompts to the same length\n    within each batch, and track the actual (unpadded) prompt lengths for loss masking.\n    \"\"\"\n\n    def __init__(\n        self,\n        tokenizer,\n        max_length=2048,\n        reason_first=True,\n        student_thinking=False,\n        teacher_thinking=True,\n    ):\n        self.tokenizer = tokenizer\n        self.max_length = max_length\n        self.reason_first = reason_first\n        self.student_thinking = student_thinking\n        self.teacher_thinking = teacher_thinking\n\n        # Prompt for reasoning about the solution before teaching\n        self.reason_first_prompt = (\n            \"\\n\\nThe reference reasoning above arrives at the correct answer. \"\n            \"Please analyze this solution and explain the key reasoning steps and problem-solving strategies employed. \"\n            \"Do NOT use <think> tags. Do NOT derive your own solution. \"\n            \"Simply analyze and explain the reference solution provided above.\\n\"\n        )\n        # Prompt for transitioning to teaching mode after reasoning\n        self.transition_prompt = (\n            \"\\n\\nAfter reading the reference solution above, make sure you truly understand \"\n            \"the reasoning behind each step — do not copy or paraphrase it. Now, using your \"\n            \"own words and independent reasoning, derive the same final answer to the problem above. \"\n            \"Think step by step, explore different approaches, and don't be afraid to backtrack \"\n            \"or reconsider if something doesn't work out:\\n\"\n        )\n\n        # Set padding side explicitly for consistency\n        print(f\"[DataCollator] Original padding_side: {self.tokenizer.padding_side}\")\n        self.tokenizer.padding_side = \"right\"\n        print(f\"[DataCollator] Set padding_side to: {self.tokenizer.padding_side}\")\n        print(f\"[DataCollator] Reason first mode: {self.reason_first}\")\n\n    def __call__(self, features):\n\n        batch_size = len(features)\n\n        # Prepare student and teacher prompts using chat template (matching evaluation)\n        student_prompts = []\n        teacher_prompts = []\n        teacher_reasoning_prompts = []  # NEW: for reason_first mode\n\n        for feature in features:\n            # Extract problem and solution from dataset\n            # Handle different possible column names\n            problem = feature[\"problem\"]\n            solution = feature[\"solution\"]\n\n            # Student prompt: just the problem with instruction (matching evaluation format)\n            student_user_message = f\"Problem: {problem}\\n\\nPlease reason step by step, and put your final answer within \\\\boxed{{}}.\"\n            student_messages = [{\"role\": \"user\", \"content\": student_user_message}]\n\n            # Apply chat template for student (matching evaluation)", "numLines": 70, "startLine": 1, "totalLines": 207}}

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

[metadata] {"filePath": "/workspace/repo/data_collator.py", "oldString": "import torch\n\n\nclass SelfDistillationDataCollator:", "newString": "import os\n\nimport torch\n\n# Which column of the training set is shown to the teacher as its privileged context.\n#   \"solution\" — the terse formal write-up (the released OPSD recipe).\n#   \"cot\"      — the reference *reasoning trace* for the same problem: a first-person\n#                derivation that explores, checks and backtracks before boxing the answer.\n# The teacher's conditioning shapes the behaviour the student inherits along its own\n# rollouts, so a process-shaped reference teaches deliberation, while a terse write-up\n# mostly teaches \"state the result\".\nTEACHER_REF = os.environ.get(\"OPSD_TEACHER_REF\", \"solution\")\nTEACHER_REF_MAX_CHARS = int(os.environ.get(\"OPSD_TEACHER_REF_MAX_CHARS\", \"8000\"))\n\n\ndef _teacher_reference(feature):\n    \"\"\"The reference text placed in the teacher's context for one example.\"\"\"\n    if TEACHER_REF == \"solution\":\n        return feature[\"solution\"]\n    if TEACHER_REF != \"cot\":\n        raise ValueError(f\"unknown OPSD_TEACHER_REF={TEACHER_REF}\")\n    trace = feature.get(\"COT_Reason\")\n    if not trace:\n        raise KeyError(\"OPSD_TEACHER_REF=cot needs the 'COT_Reason' column on the dataset\")\n    trace = str(trace).strip()\n    if len(trace) > TEACHER_REF_MAX_CHARS:\n        # Keep the head of the derivation and re-attach the ground-truth answer, so the\n        # teacher stays fully privileged even when the trace is clipped for memory.\n        answer = str(feature.get(\"Answer\", \"\")).strip()\n        trace = trace[:TEACHER_REF_MAX_CHARS].rstrip() + \" ...\"\n        if answer:\n            trace += f\"\\n\\n**Final Answer**\\n\\\\boxed{{{answer}}}\"\n    return trace\n\n\nclass SelfDistillationDataCollator:", "originalFile": "import torch\n\n\nclass SelfDistillationDataCollator:\n    \"\"\"\n    Data collator for self-distillation that creates both student and teacher inputs.\n\n    Student: sees only the problem (with chat template)\n    Teacher: sees problem + solution + transition prompt (with chat template)\n\n    To enable batch-level operations (like original GKD), we pad prompts to the same length\n    within each batch, and track the actual (unpadded) prompt lengths for loss masking.\n    \"\"\"\n\n    def __init__(\n        self,\n        tokenizer,\n        max_length=2048,\n        reason_first=True,\n        student_thinking=False,\n        teacher_thinking=True,\n    ):\n        self.tokenizer = tokenizer\n        self.max_length = max_length\n        self.reason_first = reason_first\n        self.student_thinking = student_thinking\n        self.teacher_thinking = teacher_thinking\n\n        # Prompt for reasoning about the solution before teaching\n        self.reason_first_prompt = (\n            \"\\n\\nThe reference reasoning above arrives at the correct answer. \"\n            \"Please analyze this solution and explain the key reasoning steps and problem-solving strategies employed. \"\n            \"Do NOT use <think> tags. Do NOT derive your own solution. \"\n            \"Simply analyze and explain the reference solution provided above.\\n\"\n        )\n        # Prompt for transitioning to teaching mode after reasoning\n        self.transition_prompt = (\n            \"\\n\\nAfter reading the reference solution above, make sure you truly understand \"\n            \"the reasoning behind each step — do not copy or paraphrase it. Now, using your \"\n            \"own words and independent reasoning, derive the same final answer to the problem above. \"\n            \"Think step by step, explore different approaches, and don't be afraid to backtrack \"\n            \"or reconsider if something doesn't work out:\\n\"\n        )\n\n        # Set padding side explicitly for consistency\n        print(f\"[DataCollator] Original padding_side: {self.tokenizer.padding_side}\")\n        self.tokenizer.padding_side = \"right\"\n        print(f\"[DataCollator] Set padding_side to: {self.tokenizer.padding_side}\")\n        print(f\"[DataCollator] Reason first mode: {self.reason_first}\")\n\n    def __call__(self, features):\n\n        batch_size = len(features)\n\n        # Prepare student and teacher prompts using chat template (matching evaluation)\n        student_prompts = []\n        teacher_prompts = []\n        teacher_reasoning_prompts = []  # NEW: for reason_first mode\n\n        for feature in features:\n            # Extract problem and solution from dataset\n            # Handle different possible column names\n            problem = feature[\"problem\"]\n            solution = feature[\"solution\"]\n\n            # Student prompt: just the problem with instruction (matching evaluation format)\n            student_user_message = f\"Problem: {problem}\\n\\nPlease reason step by step, and put your final answer within \\\\boxed{{}}.\"\n            student_messages = [{\"role\": \"user\", \"content\": student_user_message}]\n\n            # Apply chat template for student (matching evaluation)\n            student_prompt = self.tokenizer.apply_chat_template(\n                student_messages, tokenize=False, add_generation_prompt=True, enable_thinking=self.student_thinking\n            )\n            student_prompts.append(student_prompt)\n\n            if self.reason_first:\n                # Reasoning prompt: ask teacher to analyze the solution\n                reasoning_user_message = (\n                    f\"Problem: {problem}\\n\\n\"\n                    f\"Here is a correct reasoning to this problem:\"\n                    f\"=== Reference Reasoning Start ===\\n\"\n                    f\"{solution}\\n\"\n                    f\"=== Reference Reasoning End ===\\n\\n\"\n                    f\"{self.reason_first_prompt}\"\n                )\n                reasoning_messages = [{\"role\": \"user\", \"content\": reasoning_user_message}]\n                reasoning_prompt = self.tokenizer.apply_chat_template(\n                    reasoning_messages, tokenize=False, add_generation_prompt=True\n                )\n                teacher_reasoning_prompts.append(reasoning_prompt)\n\n                # Teacher prompt will be constructed during training after reasoning\n                # For now, create placeholder (will be replaced in training_step)\n                teacher_prompts.append(\"\")  # Placeholder\n            else:\n                # Original teacher prompt (unchanged)\n                teacher_user_message = (\n                    f\"Problem: {problem}\\n\\n\"\n                    f\"Here is a reference solution to this problem:\\n\"\n                    f\"=== Reference Solution Begin ===\\n{solution}\\n=== Reference Solution End ===\\n\"\n                    f\"{self.transition_prompt}\\n\"\n                    f\"Please reason step by step, and put your final answer within \\\\boxed{{}}.\"\n                )\n                teacher_messages = [{\"role\": \"user\", \"content\": teacher_user_message}]\n\n                # Apply chat template for teacher\n                teacher_prompt = self.tokenizer.apply_chat_template(\n                    teacher_messages, tokenize=False, add_generation_prompt=True, enable_thinking=self.teacher_thinking\n                )\n                teacher_prompts.append(teacher_prompt)\n\n        # Tokenize WITHOUT padding first to get true lengths\n        student_encoded_no_pad = self.tokenizer(\n            student_prompts,\n            padding=False,\n            truncation=True,\n            max_length=self.max_length,\n        )\n        student_prompt_lengths = [len(ids) for ids in student_encoded_no_pad[\"input_ids\"]]\n\n        # Find max lengths in this batch\n        max_student_prompt_len = max(student_prompt_lengths)\n\n        # Tokenize WITH padding to max length in batch\n        student_encoded = self.tokenizer(\n            student_prompts,\n            padding=\"max_length\",\n            truncation=True,\n            max_length=max_student_prompt_len,\n            return_tensors=\"pt\",\n        )\n\n        result = {\n            \"student_prompts\": student_encoded[\"input_ids\"],\n            \"student_prompt_attention_mask\": student_encoded[\"attention_mask\"],\n            \"student_prompt_length\": max_student_prompt_len,  # Single value for batch!\n            # Keep individual lengths for proper masking\n            \"student_prompt_lengths_per_example\": torch.tensor(student_prompt_lengths),\n        }\n\n        if self.reason_first:\n            # Tokenize reasoning prompts\n            reasoning_encoded_no_pad = self.tokenizer(\n                teacher_reasoning_prompts,\n                padding=False,\n                truncation=True,\n                max_length=self.max_length,\n            )\n            reasoning_prompt_lengths = [len(ids) for ids in reasoning_encoded_no_pad[\"input_ids\"]]\n            max_reasoning_prompt_len = max(reasoning_prompt_lengths)\n\n            reasoning_encoded = self.tokenizer(\n                teacher_reasoning_prompts,\n                padding=\"max_length\",\n                truncation=True,\n                max_length=max_reasoning_prompt_len,\n                return_tensors=\"pt\",\n            )\n\n            # Tokenize transition prompt (this will be appended after reasoning)\n            # Don't use chat template here - just the raw text\n            transition_text = f\"\\n{self.transition_prompt}\\nPlease reason step by step, and put your final answer within \\\\boxed{{}}.\"\n            transition_encoded = self.tokenizer(\n                [transition_text] * batch_size,\n                padding=False,\n                truncation=False,\n                return_tensors=\"pt\",\n            )\n\n            result.update(\n                {\n                    \"teacher_reasoning_prompts\": reasoning_encoded[\"input_ids\"],\n                    \"teacher_reasoning_attention_mask\": reasoning_encoded[\"attention_mask\"],\n                    \"teacher_reasoning_prompt_length\": max_reasoning_prompt_len,\n                    \"teacher_transition_tokens\": transition_encoded[\"input_ids\"],\n                }\n            )\n        else:\n            # Normal mode: tokenize teacher prompts\n            teacher_encoded_no_pad = self.tokenizer(\n                teacher_prompts,\n                padding=False,\n                truncation=True,\n                max_length=self.max_length,\n            )\n            teacher_prompt_lengths = [len(ids) for ids in teacher_encoded_no_pad[\"input_ids\"]]\n            max_teacher_prompt_len = max(teacher_prompt_lengths)\n\n            teacher_encoded = self.tokenizer(\n                teacher_prompts,\n                padding=\"max_length\",\n                truncation=True,\n                max_length=max_teacher_prompt_len,\n                return_tensors=\"pt\",\n            )\n\n            result.update(\n                {\n                    \"teacher_prompts\": teacher_encoded[\"input_ids\"],\n                    \"teacher_prompt_attention_mask\": teacher_encoded[\"attention_mask\"],\n                    \"teacher_prompt_length\": max_teacher_prompt_len,\n                    \"teacher_prompt_lengths_per_example\": torch.tensor(teacher_prompt_lengths),\n                }\n            )\n\n        return result\n", "structuredPatch": [{"oldStart": 1, "oldLines": 6, "newStart": 1, "newLines": 38, "lines": ["+import os", "+", " import torch", " ", "+# Which column of the training set is shown to the teacher as its privileged context.", "+#   \"solution\" — the terse formal write-up (the released OPSD recipe).", "+#   \"cot\"      — the reference *reasoning trace* for the same problem: a first-person", "+#                derivation that explores, checks and backtracks before boxing the answer.", "+# The teacher's conditioning shapes the behaviour the student inherits along its own", "+# rollouts, so a process-shaped reference teaches deliberation, while a terse write-up", "+# mostly teaches \"state the result\".", "+TEACHER_REF = os.environ.get(\"OPSD_TEACHER_REF\", \"solution\")", "+TEACHER_REF_MAX_CHARS = int(os.environ.get(\"OPSD_TEACHER_REF_MAX_CHARS\", \"8000\"))", " ", "+", "+def _teacher_reference(feature):", "+    \"\"\"The reference text placed in the teacher's context for one example.\"\"\"", "+    if TEACHER_REF == \"solution\":", "+        return feature[\"solution\"]", "+    if TEACHER_REF != \"cot\":", "+        raise ValueError(f\"unknown OPSD_TEACHER_REF={TEACHER_REF}\")", "+    trace = feature.get(\"COT_Reason\")", "+    if not trace:", "+        raise KeyError(\"OPSD_TEACHER_REF=cot needs the 'COT_Reason' column on the dataset\")", "+    trace = str(trace).strip()", "+    if len(trace) > TEACHER_REF_MAX_CHARS:", "+        # Keep the head of the derivation and re-attach the ground-truth answer, so the", "+        # teacher stays fully privileged even when the trace is clipped for memory.", "+        answer = str(feature.get(\"Answer\", \"\")).strip()", "+        trace = trace[:TEACHER_REF_MAX_CHARS].rstrip() + \" ...\"", "+        if answer:", "+            trace += f\"\\n\\n**Final Answer**\\n\\\\boxed{{{answer}}}\"", "+    return trace", "+", "+", " class SelfDistillationDataCollator:", "     \"\"\"", "     Data collator for self-distillation that creates both student and teacher inputs."]}], "userModified": false, "replaceAll": false}

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

[metadata] {"filePath": "/workspace/repo/data_collator.py", "oldString": "            problem = feature[\"problem\"]\n            solution = feature[\"solution\"]", "newString": "            problem = feature[\"problem\"]\n            solution = _teacher_reference(feature)", "originalFile": null, "structuredPatch": [{"oldStart": 93, "oldLines": 7, "newStart": 93, "newLines": 7, "lines": ["             # Extract problem and solution from dataset", "             # Handle different possible column names", "             problem = feature[\"problem\"]", "-            solution = feature[\"solution\"]", "+            solution = _teacher_reference(feature)", " ", "             # Student prompt: just the problem with instruction (matching evaluation format)", "             student_user_message = f\"Problem: {problem}\\n\\nPlease reason step by step, and put your final answer within \\\\boxed{{}}.\""]}], "userModified": false, "replaceAll": false}

[DataCollator] Set padding_side to: right
[DataCollator] Reason first mode: True
solution ref tokens: med 690 p90 1057 max 1582
cot ref tokens: med 2357 p90 3156 max 3970
===
=== [e3] train start 05:35:11  (remaining 15179s)

[stdout]
[DataCollator] Set padding_side to: right
[DataCollator] Reason first mode: True
solution ref tokens: med 690 p90 1057 max 1582
cot ref tokens: med 2357 p90 3156 max 3970
===
=== [e3] train start 05:35:11  (remaining 15179s)
[metadata] {"noOutputExpected": false}

e4 chained (CoT-trace teacher)
avg length: 1024.0
avg length: 1024.0

[stdout]
e4 chained (CoT-trace teacher)
avg length: 1024.0
avg length: 1024.0
[metadata] {"noOutputExpected": false}

=== [e3] train rc=0 05:51:19
/workspace/runs/e3/capped_seed42/checkpoint-100
=== [e3] eval start 05:51:19
14026

[stdout]
=== [e3] train rc=0 05:51:19
/workspace/runs/e3/capped_seed42/checkpoint-100
=== [e3] eval start 05:51:19
14026
[metadata] {"noOutputExpected": false}

=== [e3] train rc=0 05:51:19
/workspace/runs/e3/capped_seed42/checkpoint-100
=== [e3] eval start 05:51:19
'loss': -0.0181, 'grad_norm': 0.04035891965031624

[stdout]
=== [e3] train rc=0 05:51:19
/workspace/runs/e3/capped_seed42/checkpoint-100
=== [e3] eval start 05:51:19
'loss': -0.0181, 'grad_norm': 0.04035891965031624
[metadata] {"noOutputExpected": false}

=== [e3] eval rc=0 06:11:10
RESULT e3: avg@12=45.83 pass@12=80.00 fmt=99.4
=== [e3] done 06:11:10 (remaining 13015s)
---
[e4] NO CHECKPOINT
12785

[stdout]
=== [e3] eval rc=0 06:11:10
RESULT e3: avg@12=45.83 pass@12=80.00 fmt=99.4
=== [e3] done 06:11:10 (remaining 13015s)
---
[e4] NO CHECKPOINT
12785
[metadata] {"noOutputExpected": false}

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      : 2026-08-03_06:12:30
  host      : localhost
  rank      : 2 (local_rank: 2)
  exitcode  : 1 (pid: 18092)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
TRAIN_CKPT=
[train_capped] FATAL: no checkpoint-100 produced
=== ERRORS ===
164:[rank1]: Traceback (most recent call last):
184:[rank1]:     raise KeyError("OPSD_TEACHER_REF=cot needs the 'COT_Reason' column on the dataset")
185:[rank1]: KeyError: "OPSD_TEACHER_REF=cot needs the 'COT_Reason' column on the dataset"
186:[rank3]: Traceback (most recent call last):
206:[rank3]:     raise KeyError("OPSD_TEACHER_REF=cot needs the 'COT_Reason' column on the dataset")
207:[rank3]: KeyError: "OPSD_TEACHER_REF=cot needs the 'COT_Reason' column on the dataset"
208:[rank2]: Traceback (most recent call last):
228:[rank2]:     raise KeyError("OPSD_TEACHER_REF=cot needs the 'COT_Reason' column on the dataset")
229:[rank2]: KeyError: "OPSD_TEACHER_REF=cot needs the 'COT_Reason' column on the dataset"
230:
  0%|          | 0/100 [00:00<?, ?it/s][rank0]: Traceback (most recent call last):

[stdout]
    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      : 2026-08-03_06:12:30
  host      : localhost
  rank      : 2 (local_rank: 2)
  exitcode  : 1 (pid: 18092)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
TRAIN_CKPT=
[train_capped] FATAL: no checkpoint-100 produced
=== ERRORS ===
164:[rank1]: Traceback (most recent call last):
184:[rank1]:     raise KeyError("OPSD_TEACHER_REF=cot needs the 'COT_Reason' column on the dataset")
185:[rank1]: KeyError: "OPSD_TEACHER_REF=cot needs the 'COT_Reason' column on the dataset"
186:[rank3]: Traceback (most recent call last):
206:[rank3]:     raise KeyError("OPSD_TEACHER_REF=cot needs the 'COT_Reason' column on the dataset")
207:[rank3]: KeyError: "OPSD_TEACHER_REF=cot needs the 'COT_Reason' column on the dataset"
208:[rank2]: Traceback (most recent call last):
228:[rank2]:     raise KeyError("OPSD_TEACHER_REF=cot needs the 'COT_Reason' column on the dataset")
229:[rank2]: KeyError: "OPSD_TEACHER_REF=cot needs the 'COT_Reason' column on the dataset"
230:
  0%|          | 0/100 [00:00<?, ?it/s][rank0]: Traceback (most recent call last):
[metadata] {"noOutputExpected": false}

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

[metadata] {"filePath": "/workspace/repo/data_collator.py", "oldString": "import os\n\nimport torch\n\n# Which column of the training set is shown to the teacher as its privileged context.\n#   \"solution\" — the terse formal write-up (the released OPSD recipe).\n#   \"cot\"      — the reference *reasoning trace* for the same problem: a first-person\n#                derivation that explores, checks and backtracks before boxing the answer.\n# The teacher's conditioning shapes the behaviour the student inherits along its own\n# rollouts, so a process-shaped reference teaches deliberation, while a terse write-up\n# mostly teaches \"state the result\".\nTEACHER_REF = os.environ.get(\"OPSD_TEACHER_REF\", \"solution\")\nTEACHER_REF_MAX_CHARS = int(os.environ.get(\"OPSD_TEACHER_REF_MAX_CHARS\", \"8000\"))\n\n\ndef _teacher_reference(feature):\n    \"\"\"The reference text placed in the teacher's context for one example.\"\"\"\n    if TEACHER_REF == \"solution\":\n        return feature[\"solution\"]\n    if TEACHER_REF != \"cot\":\n        raise ValueError(f\"unknown OPSD_TEACHER_REF={TEACHER_REF}\")\n    trace = feature.get(\"COT_Reason\")\n    if not trace:\n        raise KeyError(\"OPSD_TEACHER_REF=cot needs the 'COT_Reason' column on the dataset\")\n    trace = str(trace).strip()\n    if len(trace) > TEACHER_REF_MAX_CHARS:\n        # Keep the head of the derivation and re-attach the ground-truth answer, so the\n        # teacher stays fully privileged even when the trace is clipped for memory.\n        answer = str(feature.get(\"Answer\", \"\")).strip()\n        trace = trace[:TEACHER_REF_MAX_CHARS].rstrip() + \" ...\"\n        if answer:\n            trace += f\"\\n\\n**Final Answer**\\n\\\\boxed{{{answer}}}\"\n    return trace\n\n\nclass SelfDistillationDataCollator:", "newString": "import torch\n\n\nclass SelfDistillationDataCollator:", "originalFile": null, "structuredPatch": [{"oldStart": 1, "oldLines": 38, "newStart": 1, "newLines": 6, "lines": ["-import os", "-", " import torch", " ", "-# Which column of the training set is shown to the teacher as its privileged context.", "-#   \"solution\" — the terse formal write-up (the released OPSD recipe).", "-#   \"cot\"      — the reference *reasoning trace* for the same problem: a first-person", "-#                derivation that explores, checks and backtracks before boxing the answer.", "-# The teacher's conditioning shapes the behaviour the student inherits along its own", "-# rollouts, so a process-shaped reference teaches deliberation, while a terse write-up", "-# mostly teaches \"state the result\".", "-TEACHER_REF = os.environ.get(\"OPSD_TEACHER_REF\", \"solution\")", "-TEACHER_REF_MAX_CHARS = int(os.environ.get(\"OPSD_TEACHER_REF_MAX_CHARS\", \"8000\"))", " ", "-", "-def _teacher_reference(feature):", "-    \"\"\"The reference text placed in the teacher's context for one example.\"\"\"", "-    if TEACHER_REF == \"solution\":", "-        return feature[\"solution\"]", "-    if TEACHER_REF != \"cot\":", "-        raise ValueError(f\"unknown OPSD_TEACHER_REF={TEACHER_REF}\")", "-    trace = feature.get(\"COT_Reason\")", "-    if not trace:", "-        raise KeyError(\"OPSD_TEACHER_REF=cot needs the 'COT_Reason' column on the dataset\")", "-    trace = str(trace).strip()", "-    if len(trace) > TEACHER_REF_MAX_CHARS:", "-        # Keep the head of the derivation and re-attach the ground-truth answer, so the", "-        # teacher stays fully privileged even when the trace is clipped for memory.", "-        answer = str(feature.get(\"Answer\", \"\")).strip()", "-        trace = trace[:TEACHER_REF_MAX_CHARS].rstrip() + \" ...\"", "-        if answer:", "-            trace += f\"\\n\\n**Final Answer**\\n\\\\boxed{{{answer}}}\"", "-    return trace", "-", "-", " class SelfDistillationDataCollator:", "     \"\"\"", "     Data collator for self-distillation that creates both student and teacher inputs."]}], "userModified": false, "replaceAll": false}

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

[metadata] {"filePath": "/workspace/repo/data_collator.py", "oldString": "            problem = feature[\"problem\"]\n            solution = _teacher_reference(feature)", "newString": "            problem = feature[\"problem\"]\n            # The privileged reference shown to the teacher. Which column of the dataset\n            # ends up here is decided upstream in opsd_train.py (see TEACHER_REF there).\n            solution = feature[\"solution\"]", "originalFile": "import torch\n\n\nclass SelfDistillationDataCollator:\n    \"\"\"\n    Data collator for self-distillation that creates both student and teacher inputs.\n\n    Student: sees only the problem (with chat template)\n    Teacher: sees problem + solution + transition prompt (with chat template)\n\n    To enable batch-level operations (like original GKD), we pad prompts to the same length\n    within each batch, and track the actual (unpadded) prompt lengths for loss masking.\n    \"\"\"\n\n    def __init__(\n        self,\n        tokenizer,\n        max_length=2048,\n        reason_first=True,\n        student_thinking=False,\n        teacher_thinking=True,\n    ):\n        self.tokenizer = tokenizer\n        self.max_length = max_length\n        self.reason_first = reason_first\n        self.student_thinking = student_thinking\n        self.teacher_thinking = teacher_thinking\n\n        # Prompt for reasoning about the solution before teaching\n        self.reason_first_prompt = (\n            \"\\n\\nThe reference reasoning above arrives at the correct answer. \"\n            \"Please analyze this solution and explain the key reasoning steps and problem-solving strategies employed. \"\n            \"Do NOT use <think> tags. Do NOT derive your own solution. \"\n            \"Simply analyze and explain the reference solution provided above.\\n\"\n        )\n        # Prompt for transitioning to teaching mode after reasoning\n        self.transition_prompt = (\n            \"\\n\\nAfter reading the reference solution above, make sure you truly understand \"\n            \"the reasoning behind each step — do not copy or paraphrase it. Now, using your \"\n            \"own words and independent reasoning, derive the same final answer to the problem above. \"\n            \"Think step by step, explore different approaches, and don't be afraid to backtrack \"\n            \"or reconsider if something doesn't work out:\\n\"\n        )\n\n        # Set padding side explicitly for consistency\n        print(f\"[DataCollator] Original padding_side: {self.tokenizer.padding_side}\")\n        self.tokenizer.padding_side = \"right\"\n        print(f\"[DataCollator] Set padding_side to: {self.tokenizer.padding_side}\")\n        print(f\"[DataCollator] Reason first mode: {self.reason_first}\")\n\n    def __call__(self, features):\n\n        batch_size = len(features)\n\n        # Prepare student and teacher prompts using chat template (matching evaluation)\n        student_prompts = []\n        teacher_prompts = []\n        teacher_reasoning_prompts = []  # NEW: for reason_first mode\n\n        for feature in features:\n            # Extract problem and solution from dataset\n            # Handle different possible column names\n            problem = feature[\"problem\"]\n            solution = _teacher_reference(feature)\n\n            # Student prompt: just the problem with instruction (matching evaluation format)\n            student_user_message = f\"Problem: {problem}\\n\\nPlease reason step by step, and put your final answer within \\\\boxed{{}}.\"\n            student_messages = [{\"role\": \"user\", \"content\": student_user_message}]\n\n            # Apply chat template for student (matching evaluation)\n            student_prompt = self.tokenizer.apply_chat_template(\n                student_messages, tokenize=False, add_generation_prompt=True, enable_thinking=self.student_thinking\n            )\n            student_prompts.append(student_prompt)\n\n            if self.reason_first:\n                # Reasoning prompt: ask teacher to analyze the solution\n                reasoning_user_message = (\n                    f\"Problem: {problem}\\n\\n\"\n                    f\"Here is a correct reasoning to this problem:\"\n                    f\"=== Reference Reasoning Start ===\\n\"\n                    f\"{solution}\\n\"\n                    f\"=== Reference Reasoning End ===\\n\\n\"\n                    f\"{self.reason_first_prompt}\"\n                )\n                reasoning_messages = [{\"role\": \"user\", \"content\": reasoning_user_message}]\n                reasoning_prompt = self.tokenizer.apply_chat_template(\n                    reasoning_messages, tokenize=False, add_generation_prompt=True\n                )\n                teacher_reasoning_prompts.append(reasoning_prompt)\n\n                # Teacher prompt will be constructed during training after reasoning\n                # For now, create placeholder (will be replaced in training_step)\n                teacher_prompts.append(\"\")  # Placeholder\n            else:\n                # Original teacher prompt (unchanged)\n                teacher_user_message = (\n                    f\"Problem: {problem}\\n\\n\"\n                    f\"Here is a reference solution to this problem:\\n\"\n                    f\"=== Reference Solution Begin ===\\n{solution}\\n=== Reference Solution End ===\\n\"\n                    f\"{self.transition_prompt}\\n\"\n                    f\"Please reason step by step, and put your final answer within \\\\boxed{{}}.\"\n                )\n                teacher_messages = [{\"role\": \"user\", \"content\": teacher_user_message}]\n\n                # Apply chat template for teacher\n                teacher_prompt = self.tokenizer.apply_chat_template(\n                    teacher_messages, tokenize=False, add_generation_prompt=True, enable_thinking=self.teacher_thinking\n                )\n                teacher_prompts.append(teacher_prompt)\n\n        # Tokenize WITHOUT padding first to get true lengths\n        student_encoded_no_pad = self.tokenizer(\n            student_prompts,\n            padding=False,\n            truncation=True,\n            max_length=self.max_length,\n        )\n        student_prompt_lengths = [len(ids) for ids in student_encoded_no_pad[\"input_ids\"]]\n\n        # Find max lengths in this batch\n        max_student_prompt_len = max(student_prompt_lengths)\n\n        # Tokenize WITH padding to max length in batch\n        student_encoded = self.tokenizer(\n            student_prompts,\n            padding=\"max_length\",\n            truncation=True,\n            max_length=max_student_prompt_len,\n            return_tensors=\"pt\",\n        )\n\n        result = {\n            \"student_prompts\": student_encoded[\"input_ids\"],\n            \"student_prompt_attention_mask\": student_encoded[\"attention_mask\"],\n            \"student_prompt_length\": max_student_prompt_len,  # Single value for batch!\n            # Keep individual lengths for proper masking\n            \"student_prompt_lengths_per_example\": torch.tensor(student_prompt_lengths),\n        }\n\n        if self.reason_first:\n            # Tokenize reasoning prompts\n            reasoning_encoded_no_pad = self.tokenizer(\n                teacher_reasoning_prompts,\n                padding=False,\n                truncation=True,\n                max_length=self.max_length,\n            )\n            reasoning_prompt_lengths = [len(ids) for ids in reasoning_encoded_no_pad[\"input_ids\"]]\n            max_reasoning_prompt_len = max(reasoning_prompt_lengths)\n\n            reasoning_encoded = self.tokenizer(\n                teacher_reasoning_prompts,\n                padding=\"max_length\",\n                truncation=True,\n                max_length=max_reasoning_prompt_len,\n                return_tensors=\"pt\",\n            )\n\n            # Tokenize transition prompt (this will be appended after reasoning)\n            # Don't use chat template here - just the raw text\n            transition_text = f\"\\n{self.transition_prompt}\\nPlease reason step by step, and put your final answer within \\\\boxed{{}}.\"\n            transition_encoded = self.tokenizer(\n                [transition_text] * batch_size,\n                padding=False,\n                truncation=False,\n                return_tensors=\"pt\",\n            )\n\n            result.update(\n                {\n                    \"teacher_reasoning_prompts\": reasoning_encoded[\"input_ids\"],\n                    \"teacher_reasoning_attention_mask\": reasoning_encoded[\"attention_mask\"],\n                    \"teacher_reasoning_prompt_length\": max_reasoning_prompt_len,\n                    \"teacher_transition_tokens\": transition_encoded[\"input_ids\"],\n                }\n            )\n        else:\n            # Normal mode: tokenize teacher prompts\n            teacher_encoded_no_pad = self.tokenizer(\n                teacher_prompts,\n                padding=False,\n                truncation=True,\n                max_length=self.max_length,\n            )\n            teacher_prompt_lengths = [len(ids) for ids in teacher_encoded_no_pad[\"input_ids\"]]\n            max_teacher_prompt_len = max(teacher_prompt_lengths)\n\n            teacher_encoded = self.tokenizer(\n                teacher_prompts,\n                padding=\"max_length\",\n                truncation=True,\n                max_length=max_teacher_prompt_len,\n                return_tensors=\"pt\",\n            )\n\n            result.update(\n                {\n                    \"teacher_prompts\": teacher_encoded[\"input_ids\"],\n                    \"teacher_prompt_attention_mask\": teacher_encoded[\"attention_mask\"],\n                    \"teacher_prompt_length\": max_teacher_prompt_len,\n                    \"teacher_prompt_lengths_per_example\": torch.tensor(teacher_prompt_lengths),\n                }\n            )\n\n        return result\n", "structuredPatch": [{"oldStart": 61, "oldLines": 7, "newStart": 61, "newLines": 9, "lines": ["             # Extract problem and solution from dataset", "             # Handle different possible column names", "             problem = feature[\"problem\"]", "-            solution = _teacher_reference(feature)", "+            # The privileged reference shown to the teacher. Which column of the dataset", "+            # ends up here is decided upstream in opsd_train.py (see TEACHER_REF there).", "+            solution = feature[\"solution\"]", " ", "             # Student prompt: just the problem with instruction (matching evaluation format)", "             student_user_message = f\"Problem: {problem}\\n\\nPlease reason step by step, and put your final answer within \\\\boxed{{}}.\""]}], "userModified": false, "replaceAll": false}

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

[metadata] {"filePath": "/workspace/repo/opsd_train.py", "oldString": "    teacher_thinking = script_args.teacher_thinking", "newString": "    # ---- privileged context for the teacher -------------------------------------------\n    # OPSD conditions the teacher on the dataset's terse formal write-up (\"solution\").\n    # That context reveals the answer but shows no *process*: the teacher therefore scores\n    # the student's exploratory rollout from a \"state the result\" posture, and the student\n    # inherits that posture. Every example here also ships the reference *reasoning trace*\n    # for the same problem (\"COT_Reason\"): a first-person derivation that tries things,\n    # checks them and backtracks before boxing the same ground-truth answer. Conditioning\n    # the teacher on the trace keeps the privileged information identical while making the\n    # behaviour it implies deliberative, which is what the student is scored on.\n    TEACHER_REF = os.environ.get(\"OPSD_TEACHER_REF\", \"cot\")\n    TEACHER_REF_MAX_CHARS = int(os.environ.get(\"OPSD_TEACHER_REF_MAX_CHARS\", \"8000\"))\n\n    if TEACHER_REF == \"cot\":\n        if \"COT_Reason\" not in train_dataset.column_names:\n            raise KeyError(\"OPSD_TEACHER_REF=cot needs the 'COT_Reason' column\")\n\n        def _to_trace(ex):\n            trace = str(ex[\"COT_Reason\"] or \"\").strip()\n            if not trace:\n                return {\"solution\": ex[\"solution\"]}\n            if len(trace) > TEACHER_REF_MAX_CHARS:\n                # Keep the head of the derivation, then re-attach the ground-truth answer so\n                # the teacher stays exactly as privileged as before when a trace is clipped.\n                answer = str(ex.get(\"Answer\") or \"\").strip()\n                trace = trace[:TEACHER_REF_MAX_CHARS].rstrip() + \" ...\"\n                if answer:\n                    trace += f\"\\n\\n**Final Answer**\\n\\\\boxed{{{answer}}}\"\n            return {\"solution\": trace}\n\n        # Replace the teacher's reference in place; nothing else reads this column.\n        train_dataset = train_dataset.map(_to_trace, desc=\"teacher reference <- COT_Reason\")\n        print(f\"[teacher_ref] using COT_Reason traces (cap {TEACHER_REF_MAX_CHARS} chars)\")\n    elif TEACHER_REF != \"solution\":\n        raise ValueError(f\"unknown OPSD_TEACHER_REF={TEACHER_REF}\")\n\n    teacher_thinking = script_args.teacher_thinking", "originalFile": null, "structuredPatch": [{"oldStart": 285, "oldLines": 6, "newStart": 285, "newLines": 41, "lines": ["         train_dataset = train_dataset.filter(keep)", "         print(f\"[data] filter={data_filter}: {n_before} -> {len(train_dataset)} examples\")", " ", "+    # ---- privileged context for the teacher -------------------------------------------", "+    # OPSD conditions the teacher on the dataset's terse formal write-up (\"solution\").", "+    # That context reveals the answer but shows no *process*: the teacher therefore scores", "+    # the student's exploratory rollout from a \"state the result\" posture, and the student", "+    # inherits that posture. Every example here also ships the reference *reasoning trace*", "+    # for the same problem (\"COT_Reason\"): a first-person derivation that tries things,", "+    # checks them and backtracks before boxing the same ground-truth answer. Conditioning", "+    # the teacher on the trace keeps the privileged information identical while making the", "+    # behaviour it implies deliberative, which is what the student is scored on.", "+    TEACHER_REF = os.environ.get(\"OPSD_TEACHER_REF\", \"cot\")", "+    TEACHER_REF_MAX_CHARS = int(os.environ.get(\"OPSD_TEACHER_REF_MAX_CHARS\", \"8000\"))", "+", "+    if TEACHER_REF == \"cot\":", "+        if \"COT_Reason\" not in train_dataset.column_names:", "+            raise KeyError(\"OPSD_TEACHER_REF=cot needs the 'COT_Reason' column\")", "+", "+        def _to_trace(ex):", "+            trace = str(ex[\"COT_Reason\"] or \"\").strip()", "+            if not trace:", "+                return {\"solution\": ex[\"solution\"]}", "+            if len(trace) > TEACHER_REF_MAX_CHARS:", "+                # Keep the head of the derivation, then re-attach the ground-truth answer so", "+                # the teacher stays exactly as privileged as before when a trace is clipped.", "+                answer = str(ex.get(\"Answer\") or \"\").strip()", "+                trace = trace[:TEACHER_REF_MAX_CHARS].rstrip() + \" ...\"", "+                if answer:", "+                    trace += f\"\\n\\n**Final Answer**\\n\\\\boxed{{{answer}}}\"", "+            return {\"solution\": trace}", "+", "+        # Replace the teacher's reference in place; nothing else reads this column.", "+        train_dataset = train_dataset.map(_to_trace, desc=\"teacher reference <- COT_Reason\")", "+        print(f\"[teacher_ref] using COT_Reason traces (cap {TEACHER_REF_MAX_CHARS} chars)\")", "+    elif TEACHER_REF != \"solution\":", "+        raise ValueError(f\"unknown OPSD_TEACHER_REF={TEACHER_REF}\")", "+", "     teacher_thinking = script_args.teacher_thinking", "     if \"OPSD_TEACHER_THINKING\" in os.environ:", "         teacher_thinking = os.environ[\"OPSD_TEACHER_THINKING\"].lower() in (\"1\", \"true\", \"yes\")"]}], "userModified": false, "replaceAll": false}

[teacher_ref] using COT_Reason traces (cap 8000 chars)
=== [e4] train start 06:16:11  (remaining 12715s)

[stdout]
[teacher_ref] using COT_Reason traces (cap 8000 chars)
=== [e4] train start 06:16:11  (remaining 12715s)
[metadata] {"noOutputExpected": false}

'loss': -0.0013, 'grad_norm': 0.06048591807484627
'loss': -0.0026, 'grad_norm': 0.05580249801278114
'loss': -0.0033, 'grad_norm': 0.061063364148139954
18
avg length: 1002.2
0, 68241 MiB
1, 66973 MiB

[stdout]
'loss': -0.0013, 'grad_norm': 0.06048591807484627
'loss': -0.0026, 'grad_norm': 0.05580249801278114
'loss': -0.0033, 'grad_norm': 0.061063364148139954
18
avg length: 1002.2
0, 68241 MiB
1, 66973 MiB
[metadata] {"noOutputExpected": false}