claude-opus-5

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": "STUDENT_PROMPT_STYLE = os.environ.get(\"OPSD_STUDENT_PROMPT\", \"eval\")\nTEACHER_CONTEXT = os.environ.get(\"OPSD_TEACHER_CONTEXT\", \"solution\")", "newString": "STUDENT_PROMPT_STYLE = os.environ.get(\"OPSD_STUDENT_PROMPT\", \"eval\")\nTEACHER_CONTEXT = os.environ.get(\"OPSD_TEACHER_CONTEXT\", \"solution\")\n# \"left\" keeps the prompt block flush against the completion (see the padding note\n# in __init__); \"right\" reproduces the released recipe.\nPAD_SIDE = os.environ.get(\"OPSD_PAD_SIDE\", \"left\")", "originalFile": null, "structuredPatch": [{"oldStart": 30, "oldLines": 6, "newStart": 30, "newLines": 9, "lines": [" # ---------------------------------------------------------------------------", " STUDENT_PROMPT_STYLE = os.environ.get(\"OPSD_STUDENT_PROMPT\", \"eval\")", " TEACHER_CONTEXT = os.environ.get(\"OPSD_TEACHER_CONTEXT\", \"solution\")", "+# \"left\" keeps the prompt block flush against the completion (see the padding note", "+# in __init__); \"right\" reproduces the released recipe.", "+PAD_SIDE = os.environ.get(\"OPSD_PAD_SIDE\", \"left\")", " ", " ", " class SelfDistillationDataCollator:"]}], "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": "        self.tokenizer.padding_side = \"left\"", "newString": "        self.tokenizer.padding_side = PAD_SIDE", "originalFile": null, "structuredPatch": [{"oldStart": 89, "oldLines": 7, "newStart": 89, "newLines": 7, "lines": ["         # prompt and completion contiguous (a per-row constant RoPE offset, which attention", "         # is invariant to): the same measurement drops to KL ~0.0004.", "         print(f\"[DataCollator] Original padding_side: {self.tokenizer.padding_side}\")", "-        self.tokenizer.padding_side = \"left\"", "+        self.tokenizer.padding_side = PAD_SIDE", "         print(f\"[DataCollator] Set padding_side to: {self.tokenizer.padding_side}\")", "         print(f\"[DataCollator] Reason first mode: {self.reason_first}\")", " "]}], "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": "    dataset = load_dataset(\"siyanzhao/Openthoughts_math_30k_opsd\")\n    train_dataset = dataset[\"train\"]", "newString": "    dataset = load_dataset(\"siyanzhao/Openthoughts_math_30k_opsd\")\n    train_dataset = dataset[\"train\"]\n\n    # ---------------------------------------------------------------------\n    # Data selection under a fixed optimizer budget.\n    #\n    # 100 steps at global batch 32 consumes only 3200 of the 29434 rows, so\n    # *which* rows we spend the budget on is itself a method choice. The pool is\n    # dominated by `math` (MATH-style school problems, mean verified-trace length\n    # 2219 tokens) whereas the target benchmark is competition math. On easy rows\n    # the privileged teacher and the student already agree, so the per-token KL --\n    # the entire training signal -- is near zero and the step is largely wasted.\n    #\n    # \"hard\" therefore drops the easiest source and keeps the upper half of the\n    # remaining rows by verified-trace length (a difficulty proxy: every row is\n    # already correct=True, so length reflects how much reasoning the problem\n    # needed, not whether it was solved). This re-spends the fixed budget on rows\n    # that actually carry teacher/student divergence.\n    # ---------------------------------------------------------------------\n    data_select = os.environ.get(\"OPSD_DATA_SELECT\", \"none\")\n    if data_select != \"none\":\n        n_before = len(train_dataset)\n        if data_select == \"hard\":\n            train_dataset = train_dataset.filter(lambda r: r[\"source\"] != \"math\")\n            counts = sorted(train_dataset[\"generated_token_count\"])\n            thresh = counts[len(counts) // 2]\n            train_dataset = train_dataset.filter(lambda r: r[\"generated_token_count\"] >= thresh)\n            print(f\"[OPSD data] select=hard: {n_before} -> {len(train_dataset)} rows (trace_len >= {thresh})\")\n        elif data_select == \"nomath\":\n            train_dataset = train_dataset.filter(lambda r: r[\"source\"] != \"math\")\n            print(f\"[OPSD data] select=nomath: {n_before} -> {len(train_dataset)} rows\")\n        else:\n            raise ValueError(f\"unknown OPSD_DATA_SELECT={data_select}\")", "originalFile": null, "structuredPatch": [{"oldStart": 266, "oldLines": 6, "newStart": 266, "newLines": 37, "lines": ["     dataset = load_dataset(\"siyanzhao/Openthoughts_math_30k_opsd\")", "     train_dataset = dataset[\"train\"]", " ", "+    # ---------------------------------------------------------------------", "+    # Data selection under a fixed optimizer budget.", "+    #", "+    # 100 steps at global batch 32 consumes only 3200 of the 29434 rows, so", "+    # *which* rows we spend the budget on is itself a method choice. The pool is", "+    # dominated by `math` (MATH-style school problems, mean verified-trace length", "+    # 2219 tokens) whereas the target benchmark is competition math. On easy rows", "+    # the privileged teacher and the student already agree, so the per-token KL --", "+    # the entire training signal -- is near zero and the step is largely wasted.", "+    #", "+    # \"hard\" therefore drops the easiest source and keeps the upper half of the", "+    # remaining rows by verified-trace length (a difficulty proxy: every row is", "+    # already correct=True, so length reflects how much reasoning the problem", "+    # needed, not whether it was solved). This re-spends the fixed budget on rows", "+    # that actually carry teacher/student divergence.", "+    # ---------------------------------------------------------------------", "+    data_select = os.environ.get(\"OPSD_DATA_SELECT\", \"none\")", "+    if data_select != \"none\":", "+        n_before = len(train_dataset)", "+        if data_select == \"hard\":", "+            train_dataset = train_dataset.filter(lambda r: r[\"source\"] != \"math\")", "+            counts = sorted(train_dataset[\"generated_token_count\"])", "+            thresh = counts[len(counts) // 2]", "+            train_dataset = train_dataset.filter(lambda r: r[\"generated_token_count\"] >= thresh)", "+            print(f\"[OPSD data] select=hard: {n_before} -> {len(train_dataset)} rows (trace_len >= {thresh})\")", "+        elif data_select == \"nomath\":", "+            train_dataset = train_dataset.filter(lambda r: r[\"source\"] != \"math\")", "+            print(f\"[OPSD data] select=nomath: {n_before} -> {len(train_dataset)} rows\")", "+        else:", "+            raise ValueError(f\"unknown OPSD_DATA_SELECT={data_select}\")", "+", "     trainer = OPSDTrainer(", "         model=model_args.model_name_or_path,", "         args=training_args,"]}], "userModified": false, "replaceAll": false}

total 1142
drwxr-xr-x 1 root root    360 Aug  3 04:41 .
drwxr-xr-x 1 root root    200 Aug  3 03:51 ..
drwxr-xr-x 1 root root     60 Aug  3 03:48 .live
drwxr-xr-x 1 root root    100 Aug  3 03:48 .metrics
drwxr-xr-x 1 root root    140 Aug  3 03:48 .timer
dr-xr-xr-x 1 root root     47 Aug  2 17:09 baseline
-rwxrwxr-x 1 root root   1359 Aug  2 17:09 dev_eval.sh
-rw-r--r-- 1 root root   5349 Aug  3 03:52 diag.out
-rw-r--r-- 1 root root   5740 Aug  3 03:51 diag.py
-rw-r--r-- 1 root root   4721 Aug  3 03:54 diag2.py
-rw-r--r-- 1 root root  28953 Aug  3 04:41 eval_ABD.log
-rw-rw-r-- 1 root root  16440 Aug  2 17:09 eval_official.py
drwxr-xr-x 1 root root     60 Aug  3 04:41 eval_results
-rw-rw-r-- 1 root root 681639 Aug  2 17:09 paper.pdf
-rw-r--r-- 1 root root  59022 Aug  3 03:51 paper.txt
drwxr-xr-x 1 root root    480 Aug  3 04:48 repo
drwxr-xr-x 1 root root     80 Aug  3 04:06 runs
-rw-r--r-- 1 root root 151052 Aug  3 04:23 runs_ABD.log
-rw-r--r-- 1 root root   4718 Aug  3 03:56 smoke.sh
-rw-r--r-- 1 root root 194880 Aug  3 04:03 smokeA.out
drwxr-xr-x 1 root root     32 Aug  2 17:15 submission
-rwxrwxr-x 1 root root   1357 Aug  2 17:09 timer.sh
-rwxrwxr-x 1 root root   3148 Aug  2 17:09 train_baseline.sh
-rwxrwxr-x 1 root root   4997 Aug  2 17:09 train_capped.sh
--- repo:
total 133
drwxr-xr-x 1 root root   480 Aug  3 04:48 .
drwxr-xr-x 1 root root   360 Aug  3 04:41 ..
drwxr-xr-x 1 root root    80 Aug  3 03:55 .git
-rw-r--r-- 1 root root    34 Aug  2 17:14 .gitignore
-rw-r--r-- 1 root root  7520 Aug  2 17:15 README.md
drwxr-xr-x 1 root root   320 Aug  3 04:07 __pycache__
-rw-r--r-- 1 root root   549 Aug  2 17:14 accelerate.yaml
-rw-r--r-- 1 root root 12707 Aug  3 04:48 data_collator.py
-rw-r--r-- 1 root root   509 Aug  2 17:14 environment.yml
drwxr-xr-x 1 root root    93 Aug  2 17:14 eval
-rw-r--r-- 1 root root 11445 Aug  2 17:14 grpo_train.py
-rw-r--r-- 1 root root 14015 Aug  3 04:48 opsd_train.py
-rw-r--r-- 1 root root 77225 Aug  3 04:04 opsd_trainer.py
drwxr-xr-x 1 root root  4096 Aug  2 17:14 scripts
-rw-r--r-- 1 root root  5242 Aug  2 17:14 sft_train.py
--- backups anywhere:
ls: cannot access '/workspace/*.orig': No such file or directory
ls: cannot access '/workspace/repo/*.orig': No such file or directory
ls: cannot access '/workspace/repo_orig': No such file or directory

[stdout]
total 1142
drwxr-xr-x 1 root root    360 Aug  3 04:41 .
drwxr-xr-x 1 root root    200 Aug  3 03:51 ..
drwxr-xr-x 1 root root     60 Aug  3 03:48 .live
drwxr-xr-x 1 root root    100 Aug  3 03:48 .metrics
drwxr-xr-x 1 root root    140 Aug  3 03:48 .timer
dr-xr-xr-x 1 root root     47 Aug  2 17:09 baseline
-rwxrwxr-x 1 root root   1359 Aug  2 17:09 dev_eval.sh
-rw-r--r-- 1 root root   5349 Aug  3 03:52 diag.out
-rw-r--r-- 1 root root   5740 Aug  3 03:51 diag.py
-rw-r--r-- 1 root root   4721 Aug  3 03:54 diag2.py
-rw-r--r-- 1 root root  28953 Aug  3 04:41 eval_ABD.log
-rw-rw-r-- 1 root root  16440 Aug  2 17:09 eval_official.py
drwxr-xr-x 1 root root     60 Aug  3 04:41 eval_results
-rw-rw-r-- 1 root root 681639 Aug  2 17:09 paper.pdf
-rw-r--r-- 1 root root  59022 Aug  3 03:51 paper.txt
drwxr-xr-x 1 root root    480 Aug  3 04:48 repo
drwxr-xr-x 1 root root     80 Aug  3 04:06 runs
-rw-r--r-- 1 root root 151052 Aug  3 04:23 runs_ABD.log
-rw-r--r-- 1 root root   4718 Aug  3 03:56 smoke.sh
-rw-r--r-- 1 root root 194880 Aug  3 04:03 smokeA.out
drwxr-xr-x 1 root root     32 Aug  2 17:15 submission
-rwxrwxr-x 1 root root   1357 Aug  2 17:09 timer.sh
-rwxrwxr-x 1 root root   3148 Aug  2 17:09 train_baseline.sh
-rwxrwxr-x 1 root root   4997 Aug  2 17:09 train_capped.sh
--- repo:
total 133
drwxr-xr-x 1 root root   480 Aug  3 04:48 .
drwxr-xr-x 1 root root   360 Aug  3 04:41 ..
drwxr-xr-x 1 root root    80 Aug  3 03:55 .git
-rw-r--r-- 1 root root    34 Aug  2 17:14 .gitignore
-rw-r--r-- 1 root root  7520 Aug  2 17:15 README.md
drwxr-xr-x 1 root root   320 Aug  3 04:07 __pycache__
-rw-r--r-- 1 root root   549 Aug  2 17:14 accelerate.yaml
-rw-r--r-- 1 root root 12707 Aug  3 04:48 data_collator.py
-rw-r--r-- 1 root root   509 Aug  2 17:14 environment.yml
drwxr-xr-x 1 root root    93 Aug  2 17:14 eval
-rw-r--r-- 1 root root 11445 Aug  2 17:14 grpo_train.py
-rw-r--r-- 1 root root 14015 Aug  3 04:48 opsd_train.py
-rw-r--r-- 1 root root 77225 Aug  3 04:04 opsd_trainer.py
drwxr-xr-x 1 root root  4096 Aug  2 17:14 scripts
-rw-r--r-- 1 root root  5242 Aug  2 17:14 sft_train.py
--- backups anywhere:
ls: cannot access '/workspace/*.orig': No such file or directory
ls: cannot access '/workspace/repo/*.orig': No such file or directory
ls: cannot access '/workspace/repo_orig': No such file or directory
[metadata] {"noOutputExpected": false}

README.md        |   4 +--
 data_collator.py |  60 +++++++++++++++++++++++++++++--
 opsd_train.py    |  31 ++++++++++++++++
 opsd_trainer.py  | 106 ++++++++++++++++++++++++++++++++++++++++++++++++++-----
 4 files changed, 186 insertions(+), 15 deletions(-)
=== trainer diff:
diff --git a/opsd_trainer.py b/opsd_trainer.py
index c151bd7..c536d88 100644
--- a/opsd_trainer.py
+++ b/opsd_trainer.py
@@ -189,4 +189,12 @@ class OPSDTrainer(SFTTrainer):
         self.top_k_loss = top_k_loss
         self.jsd_token_clip = jsd_token_clip
+        # "entry" = released recipe (clamp each vocabulary entry at tau).
+        # "token" = clamp the magnitude of each position's full-vocabulary divergence instead,
+        #           preserving its direction. See `generalized_jsd_loss`.
+        self.jsd_clip_mode = os.environ.get("OPSD_CLIP_MODE", "entry")
+        if self.jsd_clip_mode not in ("entry", "token"):
+            raise ValueError(f"OPSD_CLIP_MODE must be 'entry' or 'token', got {self.jsd_clip_mode}")
+        print(f"[OPSD] jsd clip mode: {self.jsd_clip_mode} (tau={self.jsd_token_clip})")
+        self._kl_stat_sum, self._kl_stat_n, self._kl_stat_hi = 0.0, 0, 0
         self.use_ema_teacher = use_ema_teacher
         self.ema_decay = ema_decay
@@ -371,4 +379,7 @@ class OPSDTrainer(SFTTrainer):
             "problem",
             "solution",
+            # long-form verified reasoning trace; used when the teacher's privileged
+            # context is the trace rather than the concise solution (see data_collator)
+            "COT_Reason",
         ]
         if self._signature_columns is None:
@@ -390,4 +401,5 @@ class OPSDTrainer(SFTTrainer):
         top_k=None,
         token_clip=None,
+        clip_mode="entry",
     ):
         """
@@ -460,6 +472,29 @@ class OPSDTrainer(SFTTrainer):
             jsd = beta * kl_teacher + (1 - beta) * kl_student
 
-        # Per-token clipping: cap each token's divergence value
-        if token_clip is not None:
+        if clip_mode == "token":
+            # Direction-preserving per-token capping.
+            #
+            # `entry` mode (the released recipe) clamps every *vocabulary entry* at tau. An
+            # entry exceeds tau exactly when the teacher puts real mass on a token the student
+            # under-weights -- i.e. precisely the entries carrying the teacher's correction --
+            # and clamping zeroes their gradient. Measured on Qwen3-1.7B, the teacher's own
+            # argmax entry is clamped at ~11% of positions, and on high-divergence positions
+            # 50-80% of the teacher's probability mass is clamped away. The surviving gradient
+            # there points at the renormalized *tail* of the teacher, so the most informative
+            # positions are the ones whose target gets corrupted.
+            #
+            # Instead, keep the full-vocabulary divergence direction at every position and cap
+            # only its magnitude: scale each position by a detached factor tau/k_n when its
+            # divergence k_n exceeds tau. The loss value still equals min(k_n, tau), so the
+            # heavy tail of stylistic tokens can no longer dominate the batch -- which is the
+            # stated purpose of the clip -- but the gradient at every position remains the true
+            # forward-KL direction, just norm-limited.
+            per_token = jsd.sum(-1)
+            if token_clip is not None:
+                scale = (token_clip / per_token.detach().clamp_min(1e-6)).clamp(max=1.0)
+                per_token = per_token * scale
+            jsd = per_token
+        elif token_clip is not None:
+            # Per-entry clipping: cap each vocabulary entry's divergence contribution
             jsd = jsd.clamp(max=token_clip)
 
@@ -742,5 +777,26 @@ class OPSDTrainer(SFTTrainer):
                 top_k=self.top_k_loss,
                 token_clip=self.jsd_token_clip,
+                clip_mode=self.jsd_clip_mode,
             )
+            # Cheap running diagnostic: the true (unclipped) per-token forward KL between the
+            # privileged teacher and the student along the rollout, i.e. the raw signal size.
+            with torch.no_grad():
+                for r in range(student_logits_for_loss.shape[0]):
+                    m = shifted_labels[r] != -100
+                    if not bool(m.any()):
+                        continue
+                    tlp = F.log_softmax(teacher_logits_for_loss[r][m] / self.temperature, dim=-1)
+                    slp = F.log_softmax(student_logits_for_loss[r][m] / self.temperature, dim=-1)
+                    k = (tlp.exp() * (tlp - slp)).sum(-1).float()
+                    self._kl_stat_sum += float(k.sum())
+                    self._kl_stat_n += int(k.numel())
+                    self._kl_stat_hi += int((k > (self.jsd_token_clip or 0.05)).sum())
+                    del tlp, slp, k
+                if self._kl_stat_n >= 20000 and self.accelerator.is_main_process:
+                    print(f"[OPSD kl] step {self.state.global_step}: mean per-token KL(T||S)="
+                          f"{self._kl_stat_sum / self._kl_stat_n:.4f} over {self._kl_stat_n} tokens, "
+                          f"frac above tau={self._kl_stat_hi / self._kl_stat_n:.3f}")
+                    self._kl_stat_sum, self._kl_stat_n, self._kl_stat_hi = 0.0, 0, 0
+
             del student_logits_for_loss, teacher_logits_for_loss
 
@@ -1385,6 +1441,14 @@ class OPSDTrainer(SFTTrainer):
                 )
 
-        # Get batch-level student prompt length
-        student_prompt_len = inputs["student_prompt_length"]
+        # Get batch-level student prompt length.
+        # With vLLM the prompts are decoded and re-tokenized inside the generation helper, so
+        # the prompt block width there is authoritative (a decode/re-encode round trip is not
+        # guaranteed to reproduce the collator's token count). Completions are always padded
+        # to exactly max_completion_length, so the prompt block is the remainder.
+        if self.use_vllm:
+            student_prompt_len = generated_ids.shape[1] - self.generation_config.max_new_tokens
+            inputs["student_prompt_length"] = student_prompt_len
+        else:
+            student_prompt_len = inputs["student_prompt_length"]
 
         # Extract generation part (same slice for all examples since prompts are padded)
@@ -1407,10 +1471,9 @@ class OPSDTrainer(SFTTrainer):
         inputs["teacher_attention_mask"] = teacher_attention_mask
 
-        # Create labels for generation tokens
-        # Mask prompt tokens (use per-example lengths for accurate masking)
+        # Create labels for generation tokens.
+        # Prompts are LEFT padded, so the whole prompt block (pads + prompt) is columns
+        # [0, student_prompt_len) for every example; mask it wholesale.
         labels = generated_ids.clone()
-        for i in range(labels.shape[0]):
-            actual_prompt_len = inputs["student_prompt_lengths_per_example"][i].item()
-            labels[i, :actual_prompt_len] = -100  # Mask actual prompt
+        labels[:, :student_prompt_len] = -100
 
         if self.processing_class.pad_token_id is not None:
@@ -1419,4 +1482,29 @@ class OPSDTrainer(SFTTrainer):
         inputs["labels"] = labels
 
+        # One-time alignment self-check: with left-padded prompts the supervised span must be
+        # exactly the sampled completion, and the prompt block must end flush against it (no
+        # pad tokens wedged in between, which would shift every completion token's RoPE
+        # position away from where it was sampled).
+        if not getattr(self, "_alignment_checked", False) and self.accelerator.is_main_process:
+            self._alignment_checked = True
+            pad_id = self.processing_class.pad_token_id
+            s_ids, t_ids = inputs["student_input_ids"], inputs["teacher_input_ids"]
+            t_len = inputs["teacher_prompt_length"]
+            print(f"\n[OPSD align] student_prompt_block={student_prompt_len} teacher_prompt_block={t_len}")
+            print(f"[OPSD align] student last prompt tokens (row0): "
+                  f"{self.processing_class.decode(s_ids[0, student_prompt_len - 6:student_prompt_len])!r}")
+            print(f"[OPSD align] teacher last prompt tokens (row0): "
+                  f"{self.processing_class.decode(t_ids[0, t_len - 6:t_len])!r}")
+            print(f"[OPSD align] supervised span (row0) starts: "
+                  f"{self.processing_class.decode(s_ids[0][labels[0] != -100][:12])!r}")
+            gap_s = int((s_ids[:, :student_prompt_len] == pad_id).sum(1).max())
+            gap_t = int((t_ids[:, :t_len] == pad_id).sum(1).max())
+            trail_s = int((s_ids[:, student_prompt_len - 1] == pad_id).sum())
+            trail_t = int((t_ids[:, t_len - 1] == pad_id).sum())
+            print(f"[OPSD align] pads inside prompt blocks: student={gap_s} teacher={gap_t} "
+                  f"(these are LEADING pads and are harmless)")
+            print(f"[OPSD align] rows whose prompt block ENDS in a pad (must be 0): "
+                  f"student={trail_s} teacher={trail_t}\n")
+
         # Log prompt and completion texts
         self._textual_logs["prompt"].extend(gather_object(prompt_texts))

[stdout]
 README.md        |   4 +--
 data_collator.py |  60 +++++++++++++++++++++++++++++--
 opsd_train.py    |  31 ++++++++++++++++
 opsd_trainer.py  | 106 ++++++++++++++++++++++++++++++++++++++++++++++++++-----
 4 files changed, 186 insertions(+), 15 deletions(-)
=== trainer diff:
diff --git a/opsd_trainer.py b/opsd_trainer.py
index c151bd7..c536d88 100644
--- a/opsd_trainer.py
+++ b/opsd_trainer.py
@@ -189,4 +189,12 @@ class OPSDTrainer(SFTTrainer):
         self.top_k_loss = top_k_loss
         self.jsd_token_clip = jsd_token_clip
+        # "entry" = released recipe (clamp each vocabulary entry at tau).
+        # "token" = clamp the magnitude of each position's full-vocabulary divergence instead,
+        #           preserving its direction. See `generalized_jsd_loss`.
+        self.jsd_clip_mode = os.environ.get("OPSD_CLIP_MODE", "entry")
+        if self.jsd_clip_mode not in ("entry", "token"):
+            raise ValueError(f"OPSD_CLIP_MODE must be 'entry' or 'token', got {self.jsd_clip_mode}")
+        print(f"[OPSD] jsd clip mode: {self.jsd_clip_mode} (tau={self.jsd_token_clip})")
+        self._kl_stat_sum, self._kl_stat_n, self._kl_stat_hi = 0.0, 0, 0
         self.use_ema_teacher = use_ema_teacher
         self.ema_decay = ema_decay
@@ -371,4 +379,7 @@ class OPSDTrainer(SFTTrainer):
             "problem",
             "solution",
+            # long-form verified reasoning trace; used when the teacher's privileged
+            # context is the trace rather than the concise solution (see data_collator)
+            "COT_Reason",
         ]
         if self._signature_columns is None:
@@ -390,4 +401,5 @@ class OPSDTrainer(SFTTrainer):
         top_k=None,
         token_clip=None,
+        clip_mode="entry",
     ):
         """
@@ -460,6 +472,29 @@ class OPSDTrainer(SFTTrainer):
             jsd = beta * kl_teacher + (1 - beta) * kl_student
 
-        # Per-token clipping: cap each token's divergence value
-        if token_clip is not None:
+        if clip_mode == "token":
+            # Direction-preserving per-token capping.
+            #
+            # `entry` mode (the released recipe) clamps every *vocabulary entry* at tau. An
+            # entry exceeds tau exactly when the teacher puts real mass on a token the student
+            # under-weights -- i.e. precisely the entries carrying the teacher's correction --
+            # and clamping zeroes their gradient. Measured on Qwen3-1.7B, the teacher's own
+            # argmax entry is clamped at ~11% of positions, and on high-divergence positions
+            # 50-80% of the teacher's probability mass is clamped away. The surviving gradient
+            # there points at the renormalized *tail* of the teacher, so the most informative
+            # positions are the ones whose target gets corrupted.
+            #
+            # Instead, keep the full-vocabulary divergence direction at every position and cap
+            # only its magnitude: scale each position by a detached factor tau/k_n when its
+            # divergence k_n exceeds tau. The loss value still equals min(k_n, tau), so the
+            # heavy tail of stylistic tokens can no longer dominate the batch -- which is the
+            # stated purpose of the clip -- but the gradient at every position remains the true
+            # forward-KL direction, just norm-limited.
+            per_token = jsd.sum(-1)
+            if token_clip is not None:
+                scale = (token_clip / per_token.detach().clamp_min(1e-6)).clamp(max=1.0)
+                per_token = per_token * scale
+            jsd = per_token
+        elif token_clip is not None:
+            # Per-entry clipping: cap each vocabulary entry's divergence contribution
             jsd = jsd.clamp(max=token_clip)
 
@@ -742,5 +777,26 @@ class OPSDTrainer(SFTTrainer):
                 top_k=self.top_k_loss,
                 token_clip=self.jsd_token_clip,
+                clip_mode=self.jsd_clip_mode,
             )
+            # Cheap running diagnostic: the true (unclipped) per-token forward KL between the
+            # privileged teacher and the student along the rollout, i.e. the raw signal size.
+            with torch.no_grad():
+                for r in range(student_logits_for_loss.shape[0]):
+                    m = shifted_labels[r] != -100
+                    if not bool(m.any()):
+                        continue
+                    tlp = F.log_softmax(teacher_logits_for_loss[r][m] / self.temperature, dim=-1)
+                    slp = F.log_softmax(student_logits_for_loss[r][m] / self.temperature, dim=-1)
+                    k = (tlp.exp() * (tlp - slp)).sum(-1).float()
+                    self._kl_stat_sum += float(k.sum())
+                    self._kl_stat_n += int(k.numel())
+                    self._kl_stat_hi += int((k > (self.jsd_token_clip or 0.05)).sum())
+                    del tlp, slp, k
+                if self._kl_stat_n >= 20000 and self.accelerator.is_main_process:
+                    print(f"[OPSD kl] step {self.state.global_step}: mean per-token KL(T||S)="
+                          f"{self._kl_stat_sum / self._kl_stat_n:.4f} over {self._kl_stat_n} tokens, "
+                          f"frac above tau={self._kl_stat_hi / self._kl_stat_n:.3f}")
+                    self._kl_stat_sum, self._kl_stat_n, self._kl_stat_hi = 0.0, 0, 0
+
             del student_logits_for_loss, teacher_logits_for_loss
 
@@ -1385,6 +1441,14 @@ class OPSDTrainer(SFTTrainer):
                 )
 
-        # Get batch-level student prompt length
-        student_prompt_len = inputs["student_prompt_length"]
+        # Get batch-level student prompt length.
+        # With vLLM the prompts are decoded and re-tokenized inside the generation helper, so
+        # the prompt block width there is authoritative (a decode/re-encode round trip is not
+        # guaranteed to reproduce the collator's token count). Completions are always padded
+        # to exactly max_completion_length, so the prompt block is the remainder.
+        if self.use_vllm:
+            student_prompt_len = generated_ids.shape[1] - self.generation_config.max_new_tokens
+            inputs["student_prompt_length"] = student_prompt_len
+        else:
+            student_prompt_len = inputs["student_prompt_length"]
 
         # Extract generation part (same slice for all examples since prompts are padded)
@@ -1407,10 +1471,9 @@ class OPSDTrainer(SFTTrainer):
         inputs["teacher_attention_mask"] = teacher_attention_mask
 
-        # Create labels for generation tokens
-        # Mask prompt tokens (use per-example lengths for accurate masking)
+        # Create labels for generation tokens.
+        # Prompts are LEFT padded, so the whole prompt block (pads + prompt) is columns
+        # [0, student_prompt_len) for every example; mask it wholesale.
         labels = generated_ids.clone()
-        for i in range(labels.shape[0]):
-            actual_prompt_len = inputs["student_prompt_lengths_per_example"][i].item()
-            labels[i, :actual_prompt_len] = -100  # Mask actual prompt
+        labels[:, :student_prompt_len] = -100
 
         if self.processing_class.pad_token_id is not None:
@@ -1419,4 +1482,29 @@ class OPSDTrainer(SFTTrainer):
         inputs["labels"] = labels
 
+        # One-time alignment self-check: with left-padded prompts the supervised span must be
+        # exactly the sampled completion, and the prompt block must end flush against it (no
+        # pad tokens wedged in between, which would shift every completion token's RoPE
+        # position away from where it was sampled).
+        if not getattr(self, "_alignment_checked", False) and self.accelerator.is_main_process:
+            self._alignment_checked = True
+            pad_id = self.processing_class.pad_token_id
+            s_ids, t_ids = inputs["student_input_ids"], inputs["teacher_input_ids"]
+            t_len = inputs["teacher_prompt_length"]
+            print(f"\n[OPSD align] student_prompt_block={student_prompt_len} teacher_prompt_block={t_len}")
+            print(f"[OPSD align] student last prompt tokens (row0): "
+                  f"{self.processing_class.decode(s_ids[0, student_prompt_len - 6:student_prompt_len])!r}")
+            print(f"[OPSD align] teacher last prompt tokens (row0): "
+                  f"{self.processing_class.decode(t_ids[0, t_len - 6:t_len])!r}")
+            print(f"[OPSD align] supervised span (row0) starts: "
+                  f"{self.processing_class.decode(s_ids[0][labels[0] != -100][:12])!r}")
+            gap_s = int((s_ids[:, :student_prompt_len] == pad_id).sum(1).max())
+            gap_t = int((t_ids[:, :t_len] == pad_id).sum(1).max())
+            trail_s = int((s_ids[:, student_prompt_len - 1] == pad_id).sum())
+            trail_t = int((t_ids[:, t_len - 1] == pad_id).sum())
+            print(f"[OPSD align] pads inside prompt blocks: student={gap_s} teacher={gap_t} "
+                  f"(these are LEADING pads and are harmless)")
+            print(f"[OPSD align] rows whose prompt block ENDS in a pad (must be 0): "
+                  f"student={trail_s} teacher={trail_t}\n")
+
         # Log prompt and completion texts
         self._textual_logs["prompt"].extend(gather_object(prompt_texts))
[metadata] {"noOutputExpected": false}

Command running in background with ID: b10s9wnju. Output is being written to: /tmp/claude-0/-workspace/5fd2f3dc-5928-49f9-b3bb-a6ebc92f09aa/tasks/b10s9wnju.output. You will be notified when it completes. To check interim output, use Read on that file path.
Session cwd remains /workspace/repo; directory changes made by the backgrounded command do not apply to subsequent commands.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "b10s9wnju", "backgroundCwdHint": "Session cwd remains /workspace/repo; directory changes made by the backgrounded command do not apply to subsequent commands."}

[OPSD data] select=hard: 29434 -> 12048 rows (trace_len >= 3048)
[DataCollator] Original padding_side: left
[DataCollator] Set padding_side to: right
[OPSD data] select=hard: 29434 -> 12048 rows (trace_len >= 3048)
[DataCollator] Original padding_side: left
[DataCollator] Set padding_side to: right
[OPSD data] select=hard: 29434 -> 12048 rows (trace_len >= 3048)
[DataCollator] Original padding_side: left
[DataCollator] Set padding_side to: right
[OPSD data] select=hard: 29434 -> 12048 rows (trace_len >= 3048)
[DataCollator] Original padding_side: left
[DataCollator] Set padding_side to: right
17812

[stdout]
[OPSD data] select=hard: 29434 -> 12048 rows (trace_len >= 3048)
[DataCollator] Original padding_side: left
[DataCollator] Set padding_side to: right
[OPSD data] select=hard: 29434 -> 12048 rows (trace_len >= 3048)
[DataCollator] Original padding_side: left
[DataCollator] Set padding_side to: right
[OPSD data] select=hard: 29434 -> 12048 rows (trace_len >= 3048)
[DataCollator] Original padding_side: left
[DataCollator] Set padding_side to: right
[OPSD data] select=hard: 29434 -> 12048 rows (trace_len >= 3048)
[DataCollator] Original padding_side: left
[DataCollator] Set padding_side to: right
17812
[metadata] {"noOutputExpected": false}

[OPSD kl] step 26: mean per-token KL(T||S)=0.0979 over 20066 tokens, frac above tau=0.353
{'loss': -0.0025, 'grad_norm': 0.04853824898600578, 'learning_rate': 5e-06, 'on_policy_loss': -0.0025, 'epoch': 0.07}
[OPSD kl] step 29: mean per-token KL(T||S)=0.0986 over 24069 tokens, frac above tau=0.321
{'loss': -0.0031, 'grad_norm': 0.054779086261987686, 'learning_rate': 5e-06, 'on_policy_loss': -0.0031, 'epoch': 0.08}
{'loss': -0.0041, 'grad_norm': 0.05320427194237709, 'learning_rate': 5e-06, 'on_policy_loss': -0.0041, 'epoch': 0.08}
[OPSD kl] step 32: mean per-token KL(T||S)=0.1105 over 22827 tokens, frac above tau=0.399

[stdout]
[OPSD kl] step 26: mean per-token KL(T||S)=0.0979 over 20066 tokens, frac above tau=0.353
{'loss': -0.0025, 'grad_norm': 0.04853824898600578, 'learning_rate': 5e-06, 'on_policy_loss': -0.0025, 'epoch': 0.07}
[OPSD kl] step 29: mean per-token KL(T||S)=0.0986 over 24069 tokens, frac above tau=0.321
{'loss': -0.0031, 'grad_norm': 0.054779086261987686, 'learning_rate': 5e-06, 'on_policy_loss': -0.0031, 'epoch': 0.08}
{'loss': -0.0041, 'grad_norm': 0.05320427194237709, 'learning_rate': 5e-06, 'on_policy_loss': -0.0041, 'epoch': 0.08}
[OPSD kl] step 32: mean per-token KL(T||S)=0.1105 over 22827 tokens, frac above tau=0.399
[metadata] {"noOutputExpected": false}

== /workspace/runs_DATA.log
[OPSD kl] step 80: mean per-token KL(T||S)=0.1814 over 20480 tokens, frac above tau=0.479
[OPSD kl] step 83: mean per-token KL(T||S)=0.1874 over 21153 tokens, frac above tau=0.471
[OPSD kl] step 86: mean per-token KL(T||S)=0.1880 over 23412 tokens, frac above tau=0.444
[OPSD kl] step 89: mean per-token KL(T||S)=0.1991 over 20480 tokens, frac above tau=0.502
/workspace/eval_DATA.log missing

[stdout]
== /workspace/runs_DATA.log
[OPSD kl] step 80: mean per-token KL(T||S)=0.1814 over 20480 tokens, frac above tau=0.479
[OPSD kl] step 83: mean per-token KL(T||S)=0.1874 over 21153 tokens, frac above tau=0.471
[OPSD kl] step 86: mean per-token KL(T||S)=0.1880 over 23412 tokens, frac above tau=0.444
[OPSD kl] step 89: mean per-token KL(T||S)=0.1991 over 20480 tokens, frac above tau=0.502
/workspace/eval_DATA.log missing
[metadata] {"noOutputExpected": false}

... delfamilyatthreescales:Qwen3-1.7B,Qwen3-4B,andQwen3-8B,usingtheinstruct-tunedversions.Fortrainingdata,weusethemathematicalreason-ingsubsetofOpenThoughts\050Guhaetal.,2025\051,samplingupto30Kproblem-solutionpairswithchain-of-thoughtreasoning.Weevaluateoncompetition-levelmathematicsbenchmarksincludingAIME2024,AIME2025,HMMT2025.Baselines.Wecompareagainsttwomethodstrainedonthesamedataset:\0501\051SFT,standardsupervised\002ne-tuningonexperttrajectories,whichcanbeseenasoff-policydistilla-tionfromamorepowerfulLLMthatgeneratedthereasoningtraces;\0502\051GRPO\050Shaoetal.,2024\051,grouprelativepolicyoptimizationwithbinary
---
... atthreescales:Qwen3-1.7B,Qwen3-4B,andQwen3-8B,usingtheinstruct-tunedversions.Fortrainingdata,weusethemathematicalreason-ingsubsetofOpenThoughts\050Guhaetal.,2025\051,samplingupto30Kproblem-solutionpairswithchain-of-thoughtreasoning.Weevaluateoncompetition-levelmathematicsbenchmarksincludingAIME2024,AIME2025,HMMT2025.Baselines.Wecompareagainsttwomethodstrainedonthesamedataset:\0501\051SFT,standardsupervised\002ne-tuningonexperttrajectories,whichcanbeseenasoff-policydistilla-tionfromamorepowerfulLLMthatgeneratedthereasoningtraces;\0502\051GRPO\050Shaoetal.,2024\051,grouprelativepolicyoptimizationwithbinaryoutcomere
---
... ndGRPOperformancetodecreaseforsometasksduetoentropycollapseinlatersteps.ForSFT,wetrainonthesamenumberofsamplesasOPSD.SFTperformancedegradesdueto\002ne-tuningonconcisereasoningsolutionsandreducesgenerationlengthattesttime,whereasOPSDtransformsthemintodenselearningsignalthroughrationalization. Method AIME24AIME25HMMT25Average Qwen3-8B Base\050Instruct\051 75.865.643.961.8 +SFT 72.364.242.959.8 +GRPO 76.468.946.764.0 +OPSD 77.8 70.8 45.8 64.8 Qwen3-4B Base\050Instruct\051 74.966.442.261.2 +SFT 70.262.343.458.6 +GRPO 75.668.144.462.7 +OPSD 76.4 68.3 46.1 63.6 Qwen3-1.7B Base\050Instruct\051 51.536.723.137.1 +SFT 48.4
---
... performancetodecreaseforsometasksduetoentropycollapseinlatersteps.ForSFT,wetrainonthesamenumberofsamplesasOPSD.SFTperformancedegradesdueto\002ne-tuningonconcisereasoningsolutionsandreducesgenerationlengthattesttime,whereasOPSDtransformsthemintodenselearningsignalthroughrationalization. Method AIME24AIME25HMMT25Average Qwen3-8B Base\050Instruct\051 75.865.643.961.8 +SFT 72.364.242.959.8 +GRPO 76.468.946.764.0 +OPSD 77.8 70.8 45.8 64.8 Qwen3-4B Base\050Instruct\051 74.966.442.261.2 +SFT 70.262.343.458.6 +GRPO 75.668.144.462.7 +OPSD 76.4 68.3 46.1 63.6 Qwen3-1.7B Base\050Instruct\051 51.536.723.137.1 +SFT 48.436.322
---
... studentgenerationlength,and\0505\051comparisonbetweenfull-vocabularylogitdistillationwithsampled-tokendistillation. 4.3.1.EFFECTOFDIVERGENCEOBJECTIVEAkeydesignchoiceinOPSDisthedivergenceusedforper-tokendistributionmatchingbetweentheprivilegedteacherandthestudent.WecompareforwardKL,reverseKL,andJSDonAIME25withQwen3-1.7BinTable3.Allob-jectivesareevaluatedunderthesamepointwiseclippingschemeforstability.ForwardKLconsistentlyyieldsthestrongestgains,improvingperformancefrom36.7to43.9atstep50andremainingabovethebaselineatstep100.Incontrast,reverseKLandJSDprovidelimitedornega-tiveimprovements.WethereforeadoptforwardKLina
---
... schemeforstability.ForwardKLconsistentlyyieldsthestrongestgains,improvingperformancefrom36.7to43.9atstep50andremainingabovethebaselineatstep100.Incontrast,reverseKLandJSDprovidelimitedornega-tiveimprovements.WethereforeadoptforwardKLinallremainingexperiments.Table3.ComparisonofdivergenceobjectivesonAIME25withQwen3-1.7B.WereportAvg@12atdifferenttrainingsteps.For-wardKLsigni\002cantlyimprovesperformanceoverthebasemodel,whilereverseKLandJSD\050\014=0:5\051showlimitedornegativegains. MethodBaseStep50Step100 ForwardKL\050KL\050p T kp S \051\05136.743.941.1 ReverseKL\050KL\050p S kp T \051\05136.737.535.0 JSD\050\014=0
---
... proportionatelylargevalues.Thismotivatesouruseofpointwiseclippingtocontrolsuchheavy-tailedcontributions.Empirically,thiscon\002gurationachievesthebestdownstreamperformance.WethereforeadopttheTM-offstudent/TM-onteachercon\002guration.Figure4.EffectofPer-TokenpointwiseKLClippingonQwen3-1.7BevaluatedonAIME24.Clippingpreventsperformancecol-lapse. 4.3.3.EFFECTOFPER-TOKENPOINTWISECLIPPINGAsshowninTable5,stylistictokenscanexhibithigherKLdivergencethanmath-relatedtokens,causingthemtodominatethetrainingsignal.Wemitigatethisissueus-ingper-tokenpointwiseclipping.AsshowninFigure4forQwen3-1.7B,clippingstabilizestrainingandpre
---
... ationalcostandmayintroducenoisyoruninformativecontinuations.Tostudythistrade-off,weconductanablationonQwen3-1.7Bbyvaryingthegenerationlengthofon-policysampledstu-dentresponsesamong1024and4096tokensandusefull-Figure5.EffectofGenerationLengthonQwen3-1.7B.Wecom-parestudentgenerationlengthof1024vs4096onAIME25andAIME24.vocabularylogitdistillation.AsshowninFigure5,in-creasingthegenerationlengthdoesnotleadtoconsistentimprovementsacrosseithertask.Weattributethistoearlytokensbeingmorecriticalforlearning:asthestudentgen-erationgrowslonger,latertokensbecomeincreasinglypre-dictabletotheteacherwhenconditionedonasuf\002ciently
---
... standmayintroducenoisyoruninformativecontinuations.Tostudythistrade-off,weconductanablationonQwen3-1.7Bbyvaryingthegenerationlengthofon-policysampledstu-dentresponsesamong1024and4096tokensandusefull-Figure5.EffectofGenerationLengthonQwen3-1.7B.Wecom-parestudentgenerationlengthof1024vs4096onAIME25andAIME24.vocabularylogitdistillation.AsshowninFigure5,in-creasingthegenerationlengthdoesnotleadtoconsistentimprovementsacrosseithertask.Weattributethistoearlytokensbeingmorecriticalforlearning:asthestudentgen-erationgrowslonger,latertokensbecomeincreasinglypre-dictabletotheteacherwhenconditionedonasuf\002cientlylongstude
---
... eduetostoringvocabulary-sizedlogitsateveryposition,indicatingatrade-offbetweenper-formanceandef\002ciency. 8  ===PAGE===   On-PolicySelf-DistillationforLargeLanguageModelsTable4.AblationondivergencecomputationstrategiesforOPSDonQwen3-4Bwith2048generationlengthfordistillation.Wereportpass@8accuracyonAIME25andHMMT25.Full-distributionobjectives\050logitdistillation\051outperformsampled-tokenobjectives. MethodVariant AIME25HMMT25 OPSDw/Full-vocabularylogitdistillation\050Agarwaletal.,2024\051 84.160.0 OPSDw/Sampled-tokendistillation\050Lu&Lab,2025\051 82.157.3 5.RelatedWorkLLMSelf-Training.Ourworkconnectstoalineofre-
---
... ===   On-PolicySelf-DistillationforLargeLanguageModelsTable4.AblationondivergencecomputationstrategiesforOPSDonQwen3-4Bwith2048generationlengthfordistillation.Wereportpass@8accuracyonAIME25andHMMT25.Full-distributionobjectives\050logitdistillation\051outperformsampled-tokenobjectives. MethodVariant AIME25HMMT25 OPSDw/Full-vocabularylogitdistillation\050Agarwaletal.,2024\051 84.160.0 OPSDw/Sampled-tokendistillation\050Lu&Lab,2025\051 82.157.3 5.RelatedWorkLLMSelf-Training.Ourworkconnectstoalineofre-searchshowingthatLLMscanimprovebygeneratingandexploitingtheirownsupervisionsignals\050Allen-Zhu&Li,2020;Xuetal.,2024b
---
... pledtrajectoriesareincorrect,thelearningsignalvanishesentirely.Incontrast,OPSDprovidesatoken-levelrewardr nateveryposition,enabling\002ne-grainedcreditassignmentevenwhenthe\002nalansweriswrong. 15  ===PAGE===    ===PAGE===   0 25 50 75 100 Gradient Update Steps 48 50 52 54 56 58 Avg@12 Accuracy (%) AIME24 0 25 50 75 100 Gradient Update Steps 34 36 38 40 42 44 46 Avg@12 Accuracy (%) AIME25 0 25 50 75 100 Gradient Update Steps 22 24 26 28 30 32 Avg@12 Accuracy (%) HMMT25 0 10 20 T o k e n s   G e n e r a t e d   ( × 1 0 6 ) 37 38 39 40 41 42 Avg@12 Accuracy (%) Average 10 30 50 70 90 Gradient Update Steps 0.0 0.2 0
---
... satoken-levelrewardr nateveryposition,enabling\002ne-grainedcreditassignmentevenwhenthe\002nalansweriswrong. 15  ===PAGE===    ===PAGE===   0 25 50 75 100 Gradient Update Steps 48 50 52 54 56 58 Avg@12 Accuracy (%) AIME24 0 25 50 75 100 Gradient Update Steps 34 36 38 40 42 44 46 Avg@12 Accuracy (%) AIME25 0 25 50 75 100 Gradient Update Steps 22 24 26 28 30 32 Avg@12 Accuracy (%) HMMT25 0 10 20 T o k e n s   G e n e r a t e d   ( × 1 0 6 ) 37 38 39 40 41 42 Avg@12 Accuracy (%) Average 10 30 50 70 90 Gradient Update Steps 0.0 0.2 0.4 0.6 0.8 1.0 Fraction of Batches Zero Reward Std Frac. of GRPO GRPO OPSD  ===PAGE==
---
... 25 0 10 20 T o k e n s   G e n e r a t e d   ( × 1 0 6 ) 37 38 39 40 41 42 Avg@12 Accuracy (%) Average 10 30 50 70 90 Gradient Update Steps 0.0 0.2 0.4 0.6 0.8 1.0 Fraction of Batches Zero Reward Std Frac. of GRPO GRPO OPSD  ===PAGE===   0 25 50 75 100 Gradient Update Steps 48 50 52 54 56 58 Avg@12 AIME24 Accuracy (%) w/o per-token KL Clipping w/ per-token KL Clipping  ===PAGE===   0 25 50 75 100 Gradient Update Steps 34 36 38 40 42 44 46 Avg@12 Accuracy (%) AIME25 (Qwen3-1.7B) Gen Length 4096 Gen Length 1024 0 25 50 75 100 Gradient Update Steps 50 52 54 56 58 60 Avg@12 Accuracy (%) AIME24 (Qwen3-1.7B) Gen Length
---
...  Fraction of Batches Zero Reward Std Frac. of GRPO GRPO OPSD  ===PAGE===   0 25 50 75 100 Gradient Update Steps 48 50 52 54 56 58 Avg@12 AIME24 Accuracy (%) w/o per-token KL Clipping w/ per-token KL Clipping  ===PAGE===   0 25 50 75 100 Gradient Update Steps 34 36 38 40 42 44 46 Avg@12 Accuracy (%) AIME25 (Qwen3-1.7B) Gen Length 4096 Gen Length 1024 0 25 50 75 100 Gradient Update Steps 50 52 54 56 58 60 Avg@12 Accuracy (%) AIME24 (Qwen3-1.7B) Gen Length 4096 Gen Length 1024  ===PAGE===  c<http://www.ams.org>003.002Copyright \050c\051 1997, 2009 American Mathematical Society \050<http://www.ams.org>\051, with Rese
---
... 58 Avg@12 AIME24 Accuracy (%) w/o per-token KL Clipping w/ per-token KL Clipping  ===PAGE===   0 25 50 75 100 Gradient Update Steps 34 36 38 40 42 44 46 Avg@12 Accuracy (%) AIME25 (Qwen3-1.7B) Gen Length 4096 Gen Length 1024 0 25 50 75 100 Gradient Update Steps 50 52 54 56 58 60 Avg@12 Accuracy (%) AIME24 (Qwen3-1.7B) Gen Length 4096 Gen Length 1024  ===PAGE===  c<http://www.ams.org>003.002Copyright \050c\051 1997, 2009 American Mathematical Society \050<http://www.ams.org>\051, with Reserved Font Name CMR7.CMR7Computer ModernMediumž"!i©ÁŸ K€5>Å£ü	d“,J^«µ…,ðÏ]‚^¢ zçÒ*	òµ+ˆkh©ÓjOwè½Üqì	ðk&,“æ_?€™ÌþɈc"
---

[stdout]
... delfamilyatthreescales:Qwen3-1.7B,Qwen3-4B,andQwen3-8B,usingtheinstruct-tunedversions.Fortrainingdata,weusethemathematicalreason-ingsubsetofOpenThoughts\050Guhaetal.,2025\051,samplingupto30Kproblem-solutionpairswithchain-of-thoughtreasoning.Weevaluateoncompetition-levelmathematicsbenchmarksincludingAIME2024,AIME2025,HMMT2025.Baselines.Wecompareagainsttwomethodstrainedonthesamedataset:\0501\051SFT,standardsupervised\002ne-tuningonexperttrajectories,whichcanbeseenasoff-policydistilla-tionfromamorepowerfulLLMthatgeneratedthereasoningtraces;\0502\051GRPO\050Shaoetal.,2024\051,grouprelativepolicyoptimizationwithbinary
---
... atthreescales:Qwen3-1.7B,Qwen3-4B,andQwen3-8B,usingtheinstruct-tunedversions.Fortrainingdata,weusethemathematicalreason-ingsubsetofOpenThoughts\050Guhaetal.,2025\051,samplingupto30Kproblem-solutionpairswithchain-of-thoughtreasoning.Weevaluateoncompetition-levelmathematicsbenchmarksincludingAIME2024,AIME2025,HMMT2025.Baselines.Wecompareagainsttwomethodstrainedonthesamedataset:\0501\051SFT,standardsupervised\002ne-tuningonexperttrajectories,whichcanbeseenasoff-policydistilla-tionfromamorepowerfulLLMthatgeneratedthereasoningtraces;\0502\051GRPO\050Shaoetal.,2024\051,grouprelativepolicyoptimizationwithbinaryoutcomere
---
... ndGRPOperformancetodecreaseforsometasksduetoentropycollapseinlatersteps.ForSFT,wetrainonthesamenumberofsamplesasOPSD.SFTperformancedegradesdueto\002ne-tuningonconcisereasoningsolutionsandreducesgenerationlengthattesttime,whereasOPSDtransformsthemintodenselearningsignalthroughrationalization. Method AIME24AIME25HMMT25Average Qwen3-8B Base\050Instruct\051 75.865.643.961.8 +SFT 72.364.242.959.8 +GRPO 76.468.946.764.0 +OPSD 77.8 70.8 45.8 64.8 Qwen3-4B Base\050Instruct\051 74.966.442.261.2 +SFT 70.262.343.458.6 +GRPO 75.668.144.462.7 +OPSD 76.4 68.3 46.1 63.6 Qwen3-1.7B Base\050Instruct\051 51.536.723.137.1 +SFT 48.4
---
... performancetodecreaseforsometasksduetoentropycollapseinlatersteps.ForSFT,wetrainonthesamenumberofsamplesasOPSD.SFTperformancedegradesdueto\002ne-tuningonconcisereasoningsolutionsandreducesgenerationlengthattesttime,whereasOPSDtransformsthemintodenselearningsignalthroughrationalization. Method AIME24AIME25HMMT25Average Qwen3-8B Base\050Instruct\051 75.865.643.961.8 +SFT 72.364.242.959.8 +GRPO 76.468.946.764.0 +OPSD 77.8 70.8 45.8 64.8 Qwen3-4B Base\050Instruct\051 74.966.442.261.2 +SFT 70.262.343.458.6 +GRPO 75.668.144.462.7 +OPSD 76.4 68.3 46.1 63.6 Qwen3-1.7B Base\050Instruct\051 51.536.723.137.1 +SFT 48.436.322
---
... studentgenerationlength,and\0505\051comparisonbetweenfull-vocabularylogitdistillationwithsampled-tokendistillation. 4.3.1.EFFECTOFDIVERGENCEOBJECTIVEAkeydesignchoiceinOPSDisthedivergenceusedforper-tokendistributionmatchingbetweentheprivilegedteacherandthestudent.WecompareforwardKL,reverseKL,andJSDonAIME25withQwen3-1.7BinTable3.Allob-jectivesareevaluatedunderthesamepointwiseclippingschemeforstability.ForwardKLconsistentlyyieldsthestrongestgains,improvingperformancefrom36.7to43.9atstep50andremainingabovethebaselineatstep100.Incontrast,reverseKLandJSDprovidelimitedornega-tiveimprovements.WethereforeadoptforwardKLina
---
... schemeforstability.ForwardKLconsistentlyyieldsthestrongestgains,improvingperformancefrom36.7to43.9atstep50andremainingabovethebaselineatstep100.Incontrast,reverseKLandJSDprovidelimitedornega-tiveimprovements.WethereforeadoptforwardKLinallremainingexperiments.Table3.ComparisonofdivergenceobjectivesonAIME25withQwen3-1.7B.WereportAvg@12atdifferenttrainingsteps.For-wardKLsigni\002cantlyimprovesperformanceoverthebasemodel,whilereverseKLandJSD\050\014=0:5\051showlimitedornegativegains. MethodBaseStep50Step100 ForwardKL\050KL\050p T kp S \051\05136.743.941.1 ReverseKL\050KL\050p S kp T \051\05136.737.535.0 JSD\050\014=0
---
... proportionatelylargevalues.Thismotivatesouruseofpointwiseclippingtocontrolsuchheavy-tailedcontributions.Empirically,thiscon\002gurationachievesthebestdownstreamperformance.WethereforeadopttheTM-offstudent/TM-onteachercon\002guration.Figure4.EffectofPer-TokenpointwiseKLClippingonQwen3-1.7BevaluatedonAIME24.Clippingpreventsperformancecol-lapse. 4.3.3.EFFECTOFPER-TOKENPOINTWISECLIPPINGAsshowninTable5,stylistictokenscanexhibithigherKLdivergencethanmath-relatedtokens,causingthemtodominatethetrainingsignal.Wemitigatethisissueus-ingper-tokenpointwiseclipping.AsshowninFigure4forQwen3-1.7B,clippingstabilizestrainingandpre
---
... ationalcostandmayintroducenoisyoruninformativecontinuations.Tostudythistrade-off,weconductanablationonQwen3-1.7Bbyvaryingthegenerationlengthofon-policysampledstu-dentresponsesamong1024and4096tokensandusefull-Figure5.EffectofGenerationLengthonQwen3-1.7B.Wecom-parestudentgenerationlengthof1024vs4096onAIME25andAIME24.vocabularylogitdistillation.AsshowninFigure5,in-creasingthegenerationlengthdoesnotleadtoconsistentimprovementsacrosseithertask.Weattributethistoearlytokensbeingmorecriticalforlearning:asthestudentgen-erationgrowslonger,latertokensbecomeincreasinglypre-dictabletotheteacherwhenconditionedonasuf\002ciently
---
... standmayintroducenoisyoruninformativecontinuations.Tostudythistrade-off,weconductanablationonQwen3-1.7Bbyvaryingthegenerationlengthofon-policysampledstu-dentresponsesamong1024and4096tokensandusefull-Figure5.EffectofGenerationLengthonQwen3-1.7B.Wecom-parestudentgenerationlengthof1024vs4096onAIME25andAIME24.vocabularylogitdistillation.AsshowninFigure5,in-creasingthegenerationlengthdoesnotleadtoconsistentimprovementsacrosseithertask.Weattributethistoearlytokensbeingmorecriticalforlearning:asthestudentgen-erationgrowslonger,latertokensbecomeincreasinglypre-dictabletotheteacherwhenconditionedonasuf\002cientlylongstude
---
... eduetostoringvocabulary-sizedlogitsateveryposition,indicatingatrade-offbetweenper-formanceandef\002ciency. 8  ===PAGE===   On-PolicySelf-DistillationforLargeLanguageModelsTable4.AblationondivergencecomputationstrategiesforOPSDonQwen3-4Bwith2048generationlengthfordistillation.Wereportpass@8accuracyonAIME25andHMMT25.Full-distributionobjectives\050logitdistillation\051outperformsampled-tokenobjectives. MethodVariant AIME25HMMT25 OPSDw/Full-vocabularylogitdistillation\050Agarwaletal.,2024\051 84.160.0 OPSDw/Sampled-tokendistillation\050Lu&Lab,2025\051 82.157.3 5.RelatedWorkLLMSelf-Training.Ourworkconnectstoalineofre-
---
... ===   On-PolicySelf-DistillationforLargeLanguageModelsTable4.AblationondivergencecomputationstrategiesforOPSDonQwen3-4Bwith2048generationlengthfordistillation.Wereportpass@8accuracyonAIME25andHMMT25.Full-distributionobjectives\050logitdistillation\051outperformsampled-tokenobjectives. MethodVariant AIME25HMMT25 OPSDw/Full-vocabularylogitdistillation\050Agarwaletal.,2024\051 84.160.0 OPSDw/Sampled-tokendistillation\050Lu&Lab,2025\051 82.157.3 5.RelatedWorkLLMSelf-Training.Ourworkconnectstoalineofre-searchshowingthatLLMscanimprovebygeneratingandexploitingtheirownsupervisionsignals\050Allen-Zhu&Li,2020;Xuetal.,2024b
---
... pledtrajectoriesareincorrect,thelearningsignalvanishesentirely.Incontrast,OPSDprovidesatoken-levelrewardr nateveryposition,enabling\002ne-grainedcreditassignmentevenwhenthe\002nalansweriswrong. 15  ===PAGE===    ===PAGE===   0 25 50 75 100 Gradient Update Steps 48 50 52 54 56 58 Avg@12 Accuracy (%) AIME24 0 25 50 75 100 Gradient Update Steps 34 36 38 40 42 44 46 Avg@12 Accuracy (%) AIME25 0 25 50 75 100 Gradient Update Steps 22 24 26 28 30 32 Avg@12 Accuracy (%) HMMT25 0 10 20 T o k e n s   G e n e r a t e d   ( × 1 0 6 ) 37 38 39 40 41 42 Avg@12 Accuracy (%) Average 10 30 50 70 90 Gradient Update Steps 0.0 0.2 0
---
... satoken-levelrewardr nateveryposition,enabling\002ne-grainedcreditassignmentevenwhenthe\002nalansweriswrong. 15  ===PAGE===    ===PAGE===   0 25 50 75 100 Gradient Update Steps 48 50 52 54 56 58 Avg@12 Accuracy (%) AIME24 0 25 50 75 100 Gradient Update Steps 34 36 38 40 42 44 46 Avg@12 Accuracy (%) AIME25 0 25 50 75 100 Gradient Update Steps 22 24 26 28 30 32 Avg@12 Accuracy (%) HMMT25 0 10 20 T o k e n s   G e n e r a t e d   ( × 1 0 6 ) 37 38 39 40 41 42 Avg@12 Accuracy (%) Average 10 30 50 70 90 Gradient Update Steps 0.0 0.2 0.4 0.6 0.8 1.0 Fraction of Batches Zero Reward Std Frac. of GRPO GRPO OPSD  ===PAGE==
---
... 25 0 10 20 T o k e n s   G e n e r a t e d   ( × 1 0 6 ) 37 38 39 40 41 42 Avg@12 Accuracy (%) Average 10 30 50 70 90 Gradient Update Steps 0.0 0.2 0.4 0.6 0.8 1.0 Fraction of Batches Zero Reward Std Frac. of GRPO GRPO OPSD  ===PAGE===   0 25 50 75 100 Gradient Update Steps 48 50 52 54 56 58 Avg@12 AIME24 Accuracy (%) w/o per-token KL Clipping w/ per-token KL Clipping  ===PAGE===   0 25 50 75 100 Gradient Update Steps 34 36 38 40 42 44 46 Avg@12 Accuracy (%) AIME25 (Qwen3-1.7B) Gen Length 4096 Gen Length 1024 0 25 50 75 100 Gradient Update Steps 50 52 54 56 58 60 Avg@12 Accuracy (%) AIME24 (Qwen3-1.7B) Gen Length
---
...  Fraction of Batches Zero Reward Std Frac. of GRPO GRPO OPSD  ===PAGE===   0 25 50 75 100 Gradient Update Steps 48 50 52 54 56 58 Avg@12 AIME24 Accuracy (%) w/o per-token KL Clipping w/ per-token KL Clipping  ===PAGE===   0 25 50 75 100 Gradient Update Steps 34 36 38 40 42 44 46 Avg@12 Accuracy (%) AIME25 (Qwen3-1.7B) Gen Length 4096 Gen Length 1024 0 25 50 75 100 Gradient Update Steps 50 52 54 56 58 60 Avg@12 Accuracy (%) AIME24 (Qwen3-1.7B) Gen Length 4096 Gen Length 1024  ===PAGE===  c<http://www.ams.org>003.002Copyright \050c\051 1997, 2009 American Mathematical Society \050<http://www.ams.org>\051, with Rese
---
... 58 Avg@12 AIME24 Accuracy (%) w/o per-token KL Clipping w/ per-token KL Clipping  ===PAGE===   0 25 50 75 100 Gradient Update Steps 34 36 38 40 42 44 46 Avg@12 Accuracy (%) AIME25 (Qwen3-1.7B) Gen Length 4096 Gen Length 1024 0 25 50 75 100 Gradient Update Steps 50 52 54 56 58 60 Avg@12 Accuracy (%) AIME24 (Qwen3-1.7B) Gen Length 4096 Gen Length 1024  ===PAGE===  c<http://www.ams.org>003.002Copyright \050c\051 1997, 2009 American Mathematical Society \050<http://www.ams.org>\051, with Reserved Font Name CMR7.CMR7Computer ModernMediumž"!i©ÁŸ K€5>Å£ü	d“,J^«µ…,ðÏ]‚^¢ zçÒ*	òµ+ˆkh©ÓjOwè½Üqì	ðk&,“æ_?€™ÌþɈc"
---
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''
=== clipping fig text ===
generationmodes:ThinkingModeon\050TM-on\051,inwhichthemodelproducesself-re\003ectivechain-of-thoughttokens,andThinkingModeoff\050TM-off\051,inwhichitgeneratesre-sponsesdirectly.Todeterminewhichcombinationyields7

===PAGE===


On-PolicySelf-DistillationforLargeLanguageModelsthemosteffectivelearningsignal,weanalyzetheforwardKLdivergenceKL\050p
T
kp
S
\051acrossallfourstudent/teachermodepairings,categorizingtokensintothreegroups:math\050numerals,operators,andmathematicalkeywords\051,style\050reasoningconnectives\051,andother.Table5reportsthemeanper-tokenKLwithineachcategory.Acrossallmodelsizes,theTM-offstudentpairedwithaTM-onteacheryieldsthelargestKLonmathtokens,in-dicatingstrongersupervisiononmathematicallyrelevanttokens.ThereportedKLvaluescorrespondtotheexpecteddivergenceoverthevocabularyateachposition;asshowninTable5,thisexpectationishighlyskewed,withstylistictokenscontributingdisproportionatelylargevalues.Thismotivatesouruseofpointwiseclippingtocontrolsuchheavy-tailedcontributions.Empirically,thiscon\002gurationachievesthebestdownstreamperformance.WethereforeadopttheTM-offstudent/TM-onteachercon\002guration.Figure4.EffectofPer-TokenpointwiseKLClippingonQwen3-1.7BevaluatedonAIME24.Clippingpreventsperformancecol-lapse.
4.3.3.EFFECTOFPER-TOKENPOINTWISECLIPPINGAsshowninTable5,stylistictokenscanexhibithigherKLdivergencethanmath-relatedtokens,causingthemtodominatethetrainingsignal.W

[stdout]
''
=== clipping fig text ===
generationmodes:ThinkingModeon\050TM-on\051,inwhichthemodelproducesself-re\003ectivechain-of-thoughttokens,andThinkingModeoff\050TM-off\051,inwhichitgeneratesre-sponsesdirectly.Todeterminewhichcombinationyields7

===PAGE===


On-PolicySelf-DistillationforLargeLanguageModelsthemosteffectivelearningsignal,weanalyzetheforwardKLdivergenceKL\050p
T
kp
S
\051acrossallfourstudent/teachermodepairings,categorizingtokensintothreegroups:math\050numerals,operators,andmathematicalkeywords\051,style\050reasoningconnectives\051,andother.Table5reportsthemeanper-tokenKLwithineachcategory.Acrossallmodelsizes,theTM-offstudentpairedwithaTM-onteacheryieldsthelargestKLonmathtokens,in-dicatingstrongersupervisiononmathematicallyrelevanttokens.ThereportedKLvaluescorrespondtotheexpecteddivergenceoverthevocabularyateachposition;asshowninTable5,thisexpectationishighlyskewed,withstylistictokenscontributingdisproportionatelylargevalues.Thismotivatesouruseofpointwiseclippingtocontrolsuchheavy-tailedcontributions.Empirically,thiscon\002gurationachievesthebestdownstreamperformance.WethereforeadopttheTM-offstudent/TM-onteachercon\002guration.Figure4.EffectofPer-TokenpointwiseKLClippingonQwen3-1.7BevaluatedonAIME24.Clippingpreventsperformancecol-lapse.
4.3.3.EFFECTOFPER-TOKENPOINTWISECLIPPINGAsshowninTable5,stylistictokenscanexhibithigherKLdivergencethanmath-relatedtokens,causingthemtodominatethetrainingsignal.W
[metadata] {"noOutputExpected": false}

'51.536.723.137.1\n+SFT\n48.436.322.735.8\n+GRPO\n51.138.323.737.7\n+OPSD\n57.2\n43.9\n29.2\n43.4esizethatearliertokensmaycontributemoretoeffectivedistillationastheycouldrepresentmorecriticalbranchingpointsinthereasoningprocess.AsshowninFigure3,OPSDachieveshighertokenle'

[stdout]
'51.536.723.137.1\n+SFT\n48.436.322.735.8\n+GRPO\n51.138.323.737.7\n+OPSD\n57.2\n43.9\n29.2\n43.4esizethatearliertokensmaycontributemoretoeffectivedistillationastheycouldrepresentmorecriticalbranchingpointsinthereasoningprocess.AsshowninFigure3,OPSDachieveshighertokenle'
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== /workspace/runs_DATA.log
{'train_runtime': 908.1918, 'train_samples_per_second': 3.523, 'train_steps_per_second': 0.11, 'train_loss': -0.007925688028335572, 'epoch': 0.27}
== /workspace/eval_DATA.log

16640

[stdout]
== /workspace/runs_DATA.log
{'train_runtime': 908.1918, 'train_samples_per_second': 3.523, 'train_steps_per_second': 0.11, 'train_loss': -0.007925688028335572, 'epoch': 0.27}
== /workspace/eval_DATA.log

16640
[metadata] {"noOutputExpected": false}

total 2
drwxr-xr-x 1 root root  32 Aug  2 17:15 .
drwxr-xr-x 1 root root 400 Aug  3 05:07 ..
-rw-r--r-- 1 root root 624 Aug  2 17:15 recipe.env
---content---
# OPSD method recipe — whitelisted hyper-parameter overrides (KEY=VALUE per line).
# Empty/all-commented == the OPSD baseline recipe on top of your /workspace/repo
# code changes. The scorer re-trains with THIS file + your repo for 100 steps.
# Budget knobs (max_steps, epochs, batch, gradient_accumulation, num_processes,
# model) are FIXED by the scorer and ignored here.
#
# learning_rate=5e-6
# lora_r=64
# lora_alpha=128
# beta=0
# jsd_token_clip=0.05
# temperature=1.1
# lmbda=1
# max_completion_length=1024        # <= 4096
# fixed_teacher=true
# use_ema_teacher=false
# reason_first=false
# teacher_thinking=false

[stdout]
total 2
drwxr-xr-x 1 root root  32 Aug  2 17:15 .
drwxr-xr-x 1 root root 400 Aug  3 05:07 ..
-rw-r--r-- 1 root root 624 Aug  2 17:15 recipe.env
---content---
# OPSD method recipe — whitelisted hyper-parameter overrides (KEY=VALUE per line).
# Empty/all-commented == the OPSD baseline recipe on top of your /workspace/repo
# code changes. The scorer re-trains with THIS file + your repo for 100 steps.
# Budget knobs (max_steps, epochs, batch, gradient_accumulation, num_processes,
# model) are FIXED by the scorer and ignored here.
#
# learning_rate=5e-6
# lora_r=64
# lora_alpha=128
# beta=0
# jsd_token_clip=0.05
# temperature=1.1
# lmbda=1
# max_completion_length=1024        # <= 4096
# fixed_teacher=true
# use_ema_teacher=false
# reason_first=false
# teacher_thinking=false
[metadata] {"noOutputExpected": false}

1	# OPSD method recipe — whitelisted hyper-parameter overrides (KEY=VALUE per line).
2	# Empty/all-commented == the OPSD baseline recipe on top of your /workspace/repo
3	# code changes. The scorer re-trains with THIS file + your repo for 100 steps.
4	# Budget knobs (max_steps, epochs, batch, gradient_accumulation, num_processes,
5	# model) are FIXED by the scorer and ignored here.
6	#
7	# learning_rate=5e-6
8	# lora_r=64
9	# lora_alpha=128
10	# beta=0
11	# jsd_token_clip=0.05
12	# temperature=1.1
13	# lmbda=1
14	# max_completion_length=1024        # <= 4096
15	# fixed_teacher=true
16	# use_ema_teacher=false
17	# reason_first=false
18	# teacher_thinking=false
19

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

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(no final yet)
last progress: Processed prompts:  57%|█████▋    | 48/84 [12:40<06:40, 11.12s/it, est. speed input: 6.14 toks/s, output: 1089

[stdout]
(no final yet)
last progress: Processed prompts:  57%|█████▋    | 48/84 [12:40<06:40, 11.12s/it, est. speed input: 6.14 toks/s, output: 1089
[metadata] {"noOutputExpected": false}

(no final yet)
15689

[stdout]
(no final yet)
15689
[metadata] {"noOutputExpected": false}

FINAL avg@12 accuracy: 52.78% (190/360 solutions correct on 30 problems)
Processed prompts: 100%|██████████| 84/84 [21:42<00:00,  7.18s/it, est. speed input: 7.64 toks/s, ou
Processed prompts: 100%|██████████| 84/84 [21:42<00:00,  7.18s/it, est. speed input: 7.64 toks/s, ou
Processed prompts: 100%|██████████| 84/84 [21:42<00:00, 15.50s/it, est. speed input: 7.64 toks/s, ou

[stdout]
FINAL avg@12 accuracy: 52.78% (190/360 solutions correct on 30 problems)
Processed prompts: 100%|██████████| 84/84 [21:42<00:00,  7.18s/it, est. speed input: 7.64 toks/s, ou
Processed prompts: 100%|██████████| 84/84 [21:42<00:00,  7.18s/it, est. speed input: 7.64 toks/s, ou
Processed prompts: 100%|██████████| 84/84 [21:42<00:00, 15.50s/it, est. speed input: 7.64 toks/s, ou
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Command running in background with ID: b0rshs5wf. Output is being written to: /tmp/claude-0/-workspace/5fd2f3dc-5928-49f9-b3bb-a6ebc92f09aa/tasks/b0rshs5wf.output. You will be notified when it completes. To check interim output, use Read on that file path.
Session cwd remains /workspace/repo; directory changes made by the backgrounded command do not apply to subsequent commands.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "b0rshs5wf", "backgroundCwdHint": "Session cwd remains /workspace/repo; directory changes made by the backgrounded command do not apply to subsequent commands."}

(Bash completed with no output)

[metadata] {"noOutputExpected": false}

bytes 140559
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Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s][DataCollator] Original padding_side: left
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[OPSD] jsd clip mode: entry (tau=0.05)
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Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s][DataCollator] Original padding_side: left
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[OPSD] jsd clip mode: entry (tau=0.05)
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[metadata] {"noOutputExpected": false}

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{'loss': -0.0001, 'grad_norm': 0.05929773673415184, 'learning_rate': 4.567701435686405e-06, 'on_policy_loss': -0.0001, 'epoch': 0.02}
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[stdout]
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{'loss': -0.0019, 'grad_norm': 0.0680401623249054, 'learning_rate': 4.3752776740761495e-06, 'on_policy_loss': -0.0019, 'epoch': 0.03}
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The file /workspace/repo/data_collator.py has been updated successfully. (file state is current in your context — no need to Read it back)

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