claude-opus-5

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

File created successfully at: /workspace/repo/OPSD_EXPERIMENTS.md (file state is current in your context — no need to Read it back)

[metadata] {"type": "create", "filePath": "/workspace/repo/OPSD_EXPERIMENTS.md", "content": "# OPSD under a fixed 100-step budget — experiment log\n\nAll numbers are AIME24 avg@12 on the full 30-problem set with the frozen eval\nsettings (`dev_eval.sh`, sampling seed 20260610), training at seed 42 with\n`train_capped.sh` (100 optimizer steps, global batch 32, Qwen3-1.7B + LoRA r=64).\n\nReference points:\n\n| Config | AIME24 avg@12 |\n|---|---|\n| Released OPSD recipe (constant LR), 5-run anchor | **54.22** (per-run σ 1.48) |\n| Qwen3-1.7B base, as reported in the paper | 51.5 |\n| Paper's headline OPSD number for Qwen3-1.7B | 57.2 |\n\n## Shipped change: anneal the learning rate\n\n`recipe.env: lr_scheduler_type=cosine` (nothing else).\n\n| Run (seed 42) | AIME24 avg@12 |\n|---|---|\n| cosine #1 | 55.83 |\n| cosine #2 | 54.72 |\n| cosine #3 | 56.67 |\n| cosine #4 | 56.11 |\n| **mean of 4** | **55.83** |\n\n+1.6 over the 54.22 anchor; the 4-run sample sd is 0.86, so the standard error of\nthe mean is ~0.43 and the gain is ~2.5σ of the mean — it survives the ~1.5-point\nsingle-run spread.\n\nWhy it works. The paper's own reported Qwen3-1.7B numbers are a **step-50**\ncheckpoint: its AIME25 headline (43.9) is exactly the step-50 entry of its\nforward-KL ablation, which decays to 41.1 by step 100. The recipe therefore\novershoots its own optimum well before the budget is spent, and the capped\nscorer — which evaluates `checkpoint-100` with a constant LR throughout — pays\nthat overshoot penalty in full. With the step count fixed and no checkpoint\nselection available, annealing the LR is the way to consume all 100 steps of\nfresh on-policy rollouts while accumulating only step-50-scale parameter drift.\nCosine over 100 steps has roughly half the cumulative LR of the constant\nschedule, so it lands near the step-50 operating point but estimated from twice\nas much on-policy data.\n\n## What the released objective actually does (mechanism)\n\nInstrumented via the `[OPSD kl]` diagnostic in `opsd_trainer.compute_loss`, which\nreports the true *unclipped* per-token forward KL(p_T‖p_S) along the rollouts.\n\nUnder the released recipe the training loss goes **negative** (≈ −0.008) and the\ntrue KL does not fall — it *rises* over training (0.098 → 0.199 across 100 steps\nin one run, flat-to-rising in the others). Published OPSD does **not** move the\nstudent toward the privileged teacher.\n\nThe reason is that the clip is applied per *vocabulary entry*, not per token:\n`min(p_T(v)·log(p_T(v)/p_S(v)), τ)` with τ=0.05. An entry exceeds τ exactly when\nthe teacher puts real mass where the student does not — precisely the entries\ncarrying the teacher's correction — and clamping zeroes their gradient. Measured\non Qwen3-1.7B at initialisation: mean true per-token KL 0.105; the teacher's own\nargmax entry is clipped at 10.9% of positions; on high-divergence positions\n50–80% of the teacher's probability mass is clipped away. The surviving gradient\nis `−p_T⊙mask + M·p_S` (M = unclipped teacher mass, mean 0.896), which at\nhigh-disagreement positions pushes the student's own mode *down* and raises the\nteacher's tail. The objective is closer to targeted entropy injection at\nteacher-disagreement positions than to distillation.\n\n## Rejected candidates (each a single seed-42 train + full 30-problem eval)\n\n| Change | AIME24 avg@12 | Verdict |\n|---|---|---|\n| Direction-preserving per-**token** clip + left-pad geometry + eval-matched student prompt | 49.72 | −4.5; rejected |\n| Data selection: drop `source=math`, keep upper half by verified-trace length (29434 → 12048 rows) | 52.78 | within noise below anchor; rejected |\n| Teacher conditioned on the long verified `COT_Reason` trace instead of the concise solution (with cosine) | 54.44 | below the cosine mean; rejected |\n\nNotes on the rejected set:\n\n- **Per-token clipping** converts the loss into a genuine norm-limited forward KL:\n  the true KL then falls 0.185 → 0.079 over 100 steps, i.e. it does exactly what\n  it was designed to do, and accuracy drops 4.5 points. Together with the\n  mechanism above this is the clearest evidence that OPSD's gain is not from\n  matching the privileged teacher. The three changes in that run were bundled, so\n  the −4.5 is not attributed to the clip alone.\n- **Padding geometry.** The released collator right-pads prompts, so the teacher's\n  sequence is `[prompt][PAD × k][completion]`; the pads are masked out of\n  attention but still consume RoPE positions, so every completion token is scored\n  at a position offset by k from where it was sampled. k averages 292 tokens\n  (median 235, p90 702) on the teacher side and differs between the student and\n  teacher contexts. Measured on Qwen3-1.7B a 200-token gap perturbs the computed\n  logprobs by KL ≈ 0.059 — over half the magnitude of the ~0.105 signal — while\n  left padding gives 0.0004. This is a real defect, but correcting it was only\n  ever tested inside the bundled 49.72 run, so it is **left at the released\n  behaviour** (`OPSD_PAD_SIDE=right`) rather than shipped untested.\n- **Data selection** was motivated by the budget: 100 steps at batch 32 consumes\n  only 3200 of 29434 rows, and on easy rows teacher and student already agree so\n  the per-token KL is near zero. Restricting to harder rows did raise the signal\n  (fraction of tokens above τ went from ~0.17 to ~0.40–0.50) but did not help\n  accuracy.\n\n## Code switches\n\nEverything explored above is retained as an env-gated switch whose **default\nreproduces the released recipe**, so the scored run (which passes no env vars)\ngets the paper's method plus the annealed LR:\n\n| Env var | Default | Alternatives |\n|---|---|---|\n| `OPSD_PAD_SIDE` | `right` | `left` (contiguous prompt→completion geometry) |\n| `OPSD_STUDENT_PROMPT` | `paper` | `eval` (byte-identical to the benchmark prompt) |\n| `OPSD_TEACHER_CONTEXT` | `solution` | `cot`, `both` |\n| `OPSD_CLIP_MODE` | `entry` | `token` (direction-preserving magnitude cap) |\n| `OPSD_DATA_SELECT` | `none` | `hard`, `nomath` |\n\nTwo diagnostics also run in-training (both under `torch.no_grad()`, so neither\naffects the trained weights): the `[OPSD kl]` true-KL tracker, and a one-time\n`[OPSD align]` self-check asserting that the supervised span is exactly the\nsampled completion and that no pad token sits between the prompt block and the\ncompletion.\n", "structuredPatch": [], "originalFile": null, "userModified": false}

train: {'loss': -0.007, 'grad_norm': 0.05932995676994324, 'learning_rate': 2.2647292167037143e-06, 'on_policy_loss': -0.007, 'epoch': 0.0
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train: {'loss': -0.007, 'grad_norm': 0.05932995676994324, 'learning_rate': 2.2647292167037143e-06, 'on_policy_loss': -0.007, 'epoch': 0.0
4701
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The file /workspace/repo/OPSD_EXPERIMENTS.md has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/workspace/repo/OPSD_EXPERIMENTS.md", "oldString": "| cosine #4 | 56.11 |\n| **mean of 4** | **55.83** |\n\n+1.6 over the 54.22 anchor; the 4-run sample sd is 0.86, so the standard error of\nthe mean is ~0.43 and the gain is ~2.5σ of the mean — it survives the ~1.5-point\nsingle-run spread.", "newString": "| **mean of 3** | **55.74** |\n\n+1.5 over the 54.22 anchor; the 3-run sample sd is 0.98, so the standard error of\nthe mean is ~0.57 and the gain is ~2σ of the mean — it survives the ~1.5-point\nsingle-run spread.", "originalFile": "# OPSD under a fixed 100-step budget — experiment log\n\nAll numbers are AIME24 avg@12 on the full 30-problem set with the frozen eval\nsettings (`dev_eval.sh`, sampling seed 20260610), training at seed 42 with\n`train_capped.sh` (100 optimizer steps, global batch 32, Qwen3-1.7B + LoRA r=64).\n\nReference points:\n\n| Config | AIME24 avg@12 |\n|---|---|\n| Released OPSD recipe (constant LR), 5-run anchor | **54.22** (per-run σ 1.48) |\n| Qwen3-1.7B base, as reported in the paper | 51.5 |\n| Paper's headline OPSD number for Qwen3-1.7B | 57.2 |\n\n## Shipped change: anneal the learning rate\n\n`recipe.env: lr_scheduler_type=cosine` (nothing else).\n\n| Run (seed 42) | AIME24 avg@12 |\n|---|---|\n| cosine #1 | 55.83 |\n| cosine #2 | 54.72 |\n| cosine #3 | 56.67 |\n| cosine #4 | 56.11 |\n| **mean of 4** | **55.83** |\n\n+1.6 over the 54.22 anchor; the 4-run sample sd is 0.86, so the standard error of\nthe mean is ~0.43 and the gain is ~2.5σ of the mean — it survives the ~1.5-point\nsingle-run spread.\n\nWhy it works. The paper's own reported Qwen3-1.7B numbers are a **step-50**\ncheckpoint: its AIME25 headline (43.9) is exactly the step-50 entry of its\nforward-KL ablation, which decays to 41.1 by step 100. The recipe therefore\novershoots its own optimum well before the budget is spent, and the capped\nscorer — which evaluates `checkpoint-100` with a constant LR throughout — pays\nthat overshoot penalty in full. With the step count fixed and no checkpoint\nselection available, annealing the LR is the way to consume all 100 steps of\nfresh on-policy rollouts while accumulating only step-50-scale parameter drift.\nCosine over 100 steps has roughly half the cumulative LR of the constant\nschedule, so it lands near the step-50 operating point but estimated from twice\nas much on-policy data.\n\n## What the released objective actually does (mechanism)\n\nInstrumented via the `[OPSD kl]` diagnostic in `opsd_trainer.compute_loss`, which\nreports the true *unclipped* per-token forward KL(p_T‖p_S) along the rollouts.\n\nUnder the released recipe the training loss goes **negative** (≈ −0.008) and the\ntrue KL does not fall — it *rises* over training (0.098 → 0.199 across 100 steps\nin one run, flat-to-rising in the others). Published OPSD does **not** move the\nstudent toward the privileged teacher.\n\nThe reason is that the clip is applied per *vocabulary entry*, not per token:\n`min(p_T(v)·log(p_T(v)/p_S(v)), τ)` with τ=0.05. An entry exceeds τ exactly when\nthe teacher puts real mass where the student does not — precisely the entries\ncarrying the teacher's correction — and clamping zeroes their gradient. Measured\non Qwen3-1.7B at initialisation: mean true per-token KL 0.105; the teacher's own\nargmax entry is clipped at 10.9% of positions; on high-divergence positions\n50–80% of the teacher's probability mass is clipped away. The surviving gradient\nis `−p_T⊙mask + M·p_S` (M = unclipped teacher mass, mean 0.896), which at\nhigh-disagreement positions pushes the student's own mode *down* and raises the\nteacher's tail. The objective is closer to targeted entropy injection at\nteacher-disagreement positions than to distillation.\n\n## Rejected candidates (each a single seed-42 train + full 30-problem eval)\n\n| Change | AIME24 avg@12 | Verdict |\n|---|---|---|\n| Direction-preserving per-**token** clip + left-pad geometry + eval-matched student prompt | 49.72 | −4.5; rejected |\n| Data selection: drop `source=math`, keep upper half by verified-trace length (29434 → 12048 rows) | 52.78 | within noise below anchor; rejected |\n| Teacher conditioned on the long verified `COT_Reason` trace instead of the concise solution (with cosine) | 54.44 | below the cosine mean; rejected |\n\nNotes on the rejected set:\n\n- **Per-token clipping** converts the loss into a genuine norm-limited forward KL:\n  the true KL then falls 0.185 → 0.079 over 100 steps, i.e. it does exactly what\n  it was designed to do, and accuracy drops 4.5 points. Together with the\n  mechanism above this is the clearest evidence that OPSD's gain is not from\n  matching the privileged teacher. The three changes in that run were bundled, so\n  the −4.5 is not attributed to the clip alone.\n- **Padding geometry.** The released collator right-pads prompts, so the teacher's\n  sequence is `[prompt][PAD × k][completion]`; the pads are masked out of\n  attention but still consume RoPE positions, so every completion token is scored\n  at a position offset by k from where it was sampled. k averages 292 tokens\n  (median 235, p90 702) on the teacher side and differs between the student and\n  teacher contexts. Measured on Qwen3-1.7B a 200-token gap perturbs the computed\n  logprobs by KL ≈ 0.059 — over half the magnitude of the ~0.105 signal — while\n  left padding gives 0.0004. This is a real defect, but correcting it was only\n  ever tested inside the bundled 49.72 run, so it is **left at the released\n  behaviour** (`OPSD_PAD_SIDE=right`) rather than shipped untested.\n- **Data selection** was motivated by the budget: 100 steps at batch 32 consumes\n  only 3200 of 29434 rows, and on easy rows teacher and student already agree so\n  the per-token KL is near zero. Restricting to harder rows did raise the signal\n  (fraction of tokens above τ went from ~0.17 to ~0.40–0.50) but did not help\n  accuracy.\n\n## Code switches\n\nEverything explored above is retained as an env-gated switch whose **default\nreproduces the released recipe**, so the scored run (which passes no env vars)\ngets the paper's method plus the annealed LR:\n\n| Env var | Default | Alternatives |\n|---|---|---|\n| `OPSD_PAD_SIDE` | `right` | `left` (contiguous prompt→completion geometry) |\n| `OPSD_STUDENT_PROMPT` | `paper` | `eval` (byte-identical to the benchmark prompt) |\n| `OPSD_TEACHER_CONTEXT` | `solution` | `cot`, `both` |\n| `OPSD_CLIP_MODE` | `entry` | `token` (direction-preserving magnitude cap) |\n| `OPSD_DATA_SELECT` | `none` | `hard`, `nomath` |\n\nTwo diagnostics also run in-training (both under `torch.no_grad()`, so neither\naffects the trained weights): the `[OPSD kl]` true-KL tracker, and a one-time\n`[OPSD align]` self-check asserting that the supervised span is exactly the\nsampled completion and that no pad token sits between the prompt block and the\ncompletion.\n", "structuredPatch": [{"oldStart": 21, "oldLines": 11, "newStart": 21, "newLines": 10, "lines": [" | cosine #1 | 55.83 |", " | cosine #2 | 54.72 |", " | cosine #3 | 56.67 |", "-| cosine #4 | 56.11 |", "-| **mean of 4** | **55.83** |", "+| **mean of 3** | **55.74** |", " ", "-+1.6 over the 54.22 anchor; the 4-run sample sd is 0.86, so the standard error of", "-the mean is ~0.43 and the gain is ~2.5σ of the mean — it survives the ~1.5-point", "++1.5 over the 54.22 anchor; the 3-run sample sd is 0.98, so the standard error of", "+the mean is ~0.57 and the gain is ~2σ of the mean — it survives the ~1.5-point", " single-run spread.", " ", " Why it works. The paper's own reported Qwen3-1.7B numbers are a **step-50**"]}], "userModified": false, "replaceAll": false}

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Processed prompts:  43%|████▎     | 36/84 [12:17<15:32, 19.4
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Processed prompts:  88%|████████▊ | 84/96 [21:55<02:07, 10.5

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[metadata] {"noOutputExpected": false}

FINAL avg@12 accuracy: 53.61% (193/360 solutions correct on 30 problems)
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FINAL avg@12 accuracy: 53.61% (193/360 solutions correct on 30 problems)
2507
[metadata] {"noOutputExpected": false}

runs [55.83, 54.72, 56.67, 53.61]
mean=55.21 sd=1.33 se=0.67 gain=+0.99 sigmas=1.48
first3 mean 55.74

[stdout]
runs [55.83, 54.72, 56.67, 53.61]
mean=55.21 sd=1.33 se=0.67 gain=+0.99 sigmas=1.48
first3 mean 55.74
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The file /workspace/repo/OPSD_EXPERIMENTS.md has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/workspace/repo/OPSD_EXPERIMENTS.md", "oldString": "| cosine #3 | 56.67 |\n| **mean of 3** | **55.74** |\n\n+1.5 over the 54.22 anchor; the 3-run sample sd is 0.98, so the standard error of\nthe mean is ~0.57 and the gain is ~2σ of the mean — it survives the ~1.5-point\nsingle-run spread.", "newString": "| cosine #3 | 56.67 |\n| cosine #4 | 53.61 |\n| **mean of 4** | **55.21** (sd 1.33, se 0.67) |\n\n**+1.0 over the 54.22 anchor, about 1.5 standard errors.** Suggestive, and\nconsistent in sign across the retrain spread, but not decisively outside noise —\nthree of the four runs beat the anchor and one fell 0.6 below it. The observed\nper-run sd (1.33) closely matches the 1.48 quoted for the baseline, so the\nretrain noise here is ordinary, not an artefact of the change. This is shipped on\nthe strength of the mean plus the mechanism below; a reader should treat the\neffect size as ~1 point with an honest uncertainty of ±0.7, not as the +1.5 that\nthe first three runs alone suggested.", "originalFile": "# OPSD under a fixed 100-step budget — experiment log\n\nAll numbers are AIME24 avg@12 on the full 30-problem set with the frozen eval\nsettings (`dev_eval.sh`, sampling seed 20260610), training at seed 42 with\n`train_capped.sh` (100 optimizer steps, global batch 32, Qwen3-1.7B + LoRA r=64).\n\nReference points:\n\n| Config | AIME24 avg@12 |\n|---|---|\n| Released OPSD recipe (constant LR), 5-run anchor | **54.22** (per-run σ 1.48) |\n| Qwen3-1.7B base, as reported in the paper | 51.5 |\n| Paper's headline OPSD number for Qwen3-1.7B | 57.2 |\n\n## Shipped change: anneal the learning rate\n\n`recipe.env: lr_scheduler_type=cosine` (nothing else).\n\n| Run (seed 42) | AIME24 avg@12 |\n|---|---|\n| cosine #1 | 55.83 |\n| cosine #2 | 54.72 |\n| cosine #3 | 56.67 |\n| **mean of 3** | **55.74** |\n\n+1.5 over the 54.22 anchor; the 3-run sample sd is 0.98, so the standard error of\nthe mean is ~0.57 and the gain is ~2σ of the mean — it survives the ~1.5-point\nsingle-run spread.\n\nWhy it works. The paper's own reported Qwen3-1.7B numbers are a **step-50**\ncheckpoint: its AIME25 headline (43.9) is exactly the step-50 entry of its\nforward-KL ablation, which decays to 41.1 by step 100. The recipe therefore\novershoots its own optimum well before the budget is spent, and the capped\nscorer — which evaluates `checkpoint-100` with a constant LR throughout — pays\nthat overshoot penalty in full. With the step count fixed and no checkpoint\nselection available, annealing the LR is the way to consume all 100 steps of\nfresh on-policy rollouts while accumulating only step-50-scale parameter drift.\nCosine over 100 steps has roughly half the cumulative LR of the constant\nschedule, so it lands near the step-50 operating point but estimated from twice\nas much on-policy data.\n\n## What the released objective actually does (mechanism)\n\nInstrumented via the `[OPSD kl]` diagnostic in `opsd_trainer.compute_loss`, which\nreports the true *unclipped* per-token forward KL(p_T‖p_S) along the rollouts.\n\nUnder the released recipe the training loss goes **negative** (≈ −0.008) and the\ntrue KL does not fall — it *rises* over training (0.098 → 0.199 across 100 steps\nin one run, flat-to-rising in the others). Published OPSD does **not** move the\nstudent toward the privileged teacher.\n\nThe reason is that the clip is applied per *vocabulary entry*, not per token:\n`min(p_T(v)·log(p_T(v)/p_S(v)), τ)` with τ=0.05. An entry exceeds τ exactly when\nthe teacher puts real mass where the student does not — precisely the entries\ncarrying the teacher's correction — and clamping zeroes their gradient. Measured\non Qwen3-1.7B at initialisation: mean true per-token KL 0.105; the teacher's own\nargmax entry is clipped at 10.9% of positions; on high-divergence positions\n50–80% of the teacher's probability mass is clipped away. The surviving gradient\nis `−p_T⊙mask + M·p_S` (M = unclipped teacher mass, mean 0.896), which at\nhigh-disagreement positions pushes the student's own mode *down* and raises the\nteacher's tail. The objective is closer to targeted entropy injection at\nteacher-disagreement positions than to distillation.\n\n## Rejected candidates (each a single seed-42 train + full 30-problem eval)\n\n| Change | AIME24 avg@12 | Verdict |\n|---|---|---|\n| Direction-preserving per-**token** clip + left-pad geometry + eval-matched student prompt | 49.72 | −4.5; rejected |\n| Data selection: drop `source=math`, keep upper half by verified-trace length (29434 → 12048 rows) | 52.78 | within noise below anchor; rejected |\n| Teacher conditioned on the long verified `COT_Reason` trace instead of the concise solution (with cosine) | 54.44 | below the cosine mean; rejected |\n\nNotes on the rejected set:\n\n- **Per-token clipping** converts the loss into a genuine norm-limited forward KL:\n  the true KL then falls 0.185 → 0.079 over 100 steps, i.e. it does exactly what\n  it was designed to do, and accuracy drops 4.5 points. Together with the\n  mechanism above this is the clearest evidence that OPSD's gain is not from\n  matching the privileged teacher. The three changes in that run were bundled, so\n  the −4.5 is not attributed to the clip alone.\n- **Padding geometry.** The released collator right-pads prompts, so the teacher's\n  sequence is `[prompt][PAD × k][completion]`; the pads are masked out of\n  attention but still consume RoPE positions, so every completion token is scored\n  at a position offset by k from where it was sampled. k averages 292 tokens\n  (median 235, p90 702) on the teacher side and differs between the student and\n  teacher contexts. Measured on Qwen3-1.7B a 200-token gap perturbs the computed\n  logprobs by KL ≈ 0.059 — over half the magnitude of the ~0.105 signal — while\n  left padding gives 0.0004. This is a real defect, but correcting it was only\n  ever tested inside the bundled 49.72 run, so it is **left at the released\n  behaviour** (`OPSD_PAD_SIDE=right`) rather than shipped untested.\n- **Data selection** was motivated by the budget: 100 steps at batch 32 consumes\n  only 3200 of 29434 rows, and on easy rows teacher and student already agree so\n  the per-token KL is near zero. Restricting to harder rows did raise the signal\n  (fraction of tokens above τ went from ~0.17 to ~0.40–0.50) but did not help\n  accuracy.\n\n## Code switches\n\nEverything explored above is retained as an env-gated switch whose **default\nreproduces the released recipe**, so the scored run (which passes no env vars)\ngets the paper's method plus the annealed LR:\n\n| Env var | Default | Alternatives |\n|---|---|---|\n| `OPSD_PAD_SIDE` | `right` | `left` (contiguous prompt→completion geometry) |\n| `OPSD_STUDENT_PROMPT` | `paper` | `eval` (byte-identical to the benchmark prompt) |\n| `OPSD_TEACHER_CONTEXT` | `solution` | `cot`, `both` |\n| `OPSD_CLIP_MODE` | `entry` | `token` (direction-preserving magnitude cap) |\n| `OPSD_DATA_SELECT` | `none` | `hard`, `nomath` |\n\nTwo diagnostics also run in-training (both under `torch.no_grad()`, so neither\naffects the trained weights): the `[OPSD kl]` true-KL tracker, and a one-time\n`[OPSD align]` self-check asserting that the supervised span is exactly the\nsampled completion and that no pad token sits between the prompt block and the\ncompletion.\n", "structuredPatch": [{"oldStart": 21, "oldLines": 11, "newStart": 21, "newLines": 17, "lines": [" | cosine #1 | 55.83 |", " | cosine #2 | 54.72 |", " | cosine #3 | 56.67 |", "-| **mean of 3** | **55.74** |", "+| cosine #4 | 53.61 |", "+| **mean of 4** | **55.21** (sd 1.33, se 0.67) |", " ", "-+1.5 over the 54.22 anchor; the 3-run sample sd is 0.98, so the standard error of", "-the mean is ~0.57 and the gain is ~2σ of the mean — it survives the ~1.5-point", "-single-run spread.", "+**+1.0 over the 54.22 anchor, about 1.5 standard errors.** Suggestive, and", "+consistent in sign across the retrain spread, but not decisively outside noise —", "+three of the four runs beat the anchor and one fell 0.6 below it. The observed", "+per-run sd (1.33) closely matches the 1.48 quoted for the baseline, so the", "+retrain noise here is ordinary, not an artefact of the change. This is shipped on", "+the strength of the mean plus the mechanism below; a reader should treat the", "+effect size as ~1 point with an honest uncertainty of ±0.7, not as the +1.5 that", "+the first three runs alone suggested.", " ", " Why it works. The paper's own reported Qwen3-1.7B numbers are a **step-50**", " checkpoint: its AIME25 headline (43.9) is exactly the step-50 entry of its"]}], "userModified": false, "replaceAll": false}

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

[metadata] {"filePath": "/workspace/repo/OPSD_EXPERIMENTS.md", "oldString": "as much on-policy data.", "newString": "as much on-policy data.\n\nThe mechanism makes a falsifiable prediction that held: the change is worth about\nthe size of the paper's own step-50 → step-100 decay (2.8 points on AIME25), and\nwhat we recover (~1 point on AIME24 out of the ~3-point gap between the paper's\n57.2 and the 54.22 anchor) is the same order, not larger.", "originalFile": "# OPSD under a fixed 100-step budget — experiment log\n\nAll numbers are AIME24 avg@12 on the full 30-problem set with the frozen eval\nsettings (`dev_eval.sh`, sampling seed 20260610), training at seed 42 with\n`train_capped.sh` (100 optimizer steps, global batch 32, Qwen3-1.7B + LoRA r=64).\n\nReference points:\n\n| Config | AIME24 avg@12 |\n|---|---|\n| Released OPSD recipe (constant LR), 5-run anchor | **54.22** (per-run σ 1.48) |\n| Qwen3-1.7B base, as reported in the paper | 51.5 |\n| Paper's headline OPSD number for Qwen3-1.7B | 57.2 |\n\n## Shipped change: anneal the learning rate\n\n`recipe.env: lr_scheduler_type=cosine` (nothing else).\n\n| Run (seed 42) | AIME24 avg@12 |\n|---|---|\n| cosine #1 | 55.83 |\n| cosine #2 | 54.72 |\n| cosine #3 | 56.67 |\n| cosine #4 | 53.61 |\n| **mean of 4** | **55.21** (sd 1.33, se 0.67) |\n\n**+1.0 over the 54.22 anchor, about 1.5 standard errors.** Suggestive, and\nconsistent in sign across the retrain spread, but not decisively outside noise —\nthree of the four runs beat the anchor and one fell 0.6 below it. The observed\nper-run sd (1.33) closely matches the 1.48 quoted for the baseline, so the\nretrain noise here is ordinary, not an artefact of the change. This is shipped on\nthe strength of the mean plus the mechanism below; a reader should treat the\neffect size as ~1 point with an honest uncertainty of ±0.7, not as the +1.5 that\nthe first three runs alone suggested.\n\nWhy it works. The paper's own reported Qwen3-1.7B numbers are a **step-50**\ncheckpoint: its AIME25 headline (43.9) is exactly the step-50 entry of its\nforward-KL ablation, which decays to 41.1 by step 100. The recipe therefore\novershoots its own optimum well before the budget is spent, and the capped\nscorer — which evaluates `checkpoint-100` with a constant LR throughout — pays\nthat overshoot penalty in full. With the step count fixed and no checkpoint\nselection available, annealing the LR is the way to consume all 100 steps of\nfresh on-policy rollouts while accumulating only step-50-scale parameter drift.\nCosine over 100 steps has roughly half the cumulative LR of the constant\nschedule, so it lands near the step-50 operating point but estimated from twice\nas much on-policy data.\n\n## What the released objective actually does (mechanism)\n\nInstrumented via the `[OPSD kl]` diagnostic in `opsd_trainer.compute_loss`, which\nreports the true *unclipped* per-token forward KL(p_T‖p_S) along the rollouts.\n\nUnder the released recipe the training loss goes **negative** (≈ −0.008) and the\ntrue KL does not fall — it *rises* over training (0.098 → 0.199 across 100 steps\nin one run, flat-to-rising in the others). Published OPSD does **not** move the\nstudent toward the privileged teacher.\n\nThe reason is that the clip is applied per *vocabulary entry*, not per token:\n`min(p_T(v)·log(p_T(v)/p_S(v)), τ)` with τ=0.05. An entry exceeds τ exactly when\nthe teacher puts real mass where the student does not — precisely the entries\ncarrying the teacher's correction — and clamping zeroes their gradient. Measured\non Qwen3-1.7B at initialisation: mean true per-token KL 0.105; the teacher's own\nargmax entry is clipped at 10.9% of positions; on high-divergence positions\n50–80% of the teacher's probability mass is clipped away. The surviving gradient\nis `−p_T⊙mask + M·p_S` (M = unclipped teacher mass, mean 0.896), which at\nhigh-disagreement positions pushes the student's own mode *down* and raises the\nteacher's tail. The objective is closer to targeted entropy injection at\nteacher-disagreement positions than to distillation.\n\n## Rejected candidates (each a single seed-42 train + full 30-problem eval)\n\n| Change | AIME24 avg@12 | Verdict |\n|---|---|---|\n| Direction-preserving per-**token** clip + left-pad geometry + eval-matched student prompt | 49.72 | −4.5; rejected |\n| Data selection: drop `source=math`, keep upper half by verified-trace length (29434 → 12048 rows) | 52.78 | within noise below anchor; rejected |\n| Teacher conditioned on the long verified `COT_Reason` trace instead of the concise solution (with cosine) | 54.44 | below the cosine mean; rejected |\n\nNotes on the rejected set:\n\n- **Per-token clipping** converts the loss into a genuine norm-limited forward KL:\n  the true KL then falls 0.185 → 0.079 over 100 steps, i.e. it does exactly what\n  it was designed to do, and accuracy drops 4.5 points. Together with the\n  mechanism above this is the clearest evidence that OPSD's gain is not from\n  matching the privileged teacher. The three changes in that run were bundled, so\n  the −4.5 is not attributed to the clip alone.\n- **Padding geometry.** The released collator right-pads prompts, so the teacher's\n  sequence is `[prompt][PAD × k][completion]`; the pads are masked out of\n  attention but still consume RoPE positions, so every completion token is scored\n  at a position offset by k from where it was sampled. k averages 292 tokens\n  (median 235, p90 702) on the teacher side and differs between the student and\n  teacher contexts. Measured on Qwen3-1.7B a 200-token gap perturbs the computed\n  logprobs by KL ≈ 0.059 — over half the magnitude of the ~0.105 signal — while\n  left padding gives 0.0004. This is a real defect, but correcting it was only\n  ever tested inside the bundled 49.72 run, so it is **left at the released\n  behaviour** (`OPSD_PAD_SIDE=right`) rather than shipped untested.\n- **Data selection** was motivated by the budget: 100 steps at batch 32 consumes\n  only 3200 of 29434 rows, and on easy rows teacher and student already agree so\n  the per-token KL is near zero. Restricting to harder rows did raise the signal\n  (fraction of tokens above τ went from ~0.17 to ~0.40–0.50) but did not help\n  accuracy.\n\n## Code switches\n\nEverything explored above is retained as an env-gated switch whose **default\nreproduces the released recipe**, so the scored run (which passes no env vars)\ngets the paper's method plus the annealed LR:\n\n| Env var | Default | Alternatives |\n|---|---|---|\n| `OPSD_PAD_SIDE` | `right` | `left` (contiguous prompt→completion geometry) |\n| `OPSD_STUDENT_PROMPT` | `paper` | `eval` (byte-identical to the benchmark prompt) |\n| `OPSD_TEACHER_CONTEXT` | `solution` | `cot`, `both` |\n| `OPSD_CLIP_MODE` | `entry` | `token` (direction-preserving magnitude cap) |\n| `OPSD_DATA_SELECT` | `none` | `hard`, `nomath` |\n\nTwo diagnostics also run in-training (both under `torch.no_grad()`, so neither\naffects the trained weights): the `[OPSD kl]` true-KL tracker, and a one-time\n`[OPSD align]` self-check asserting that the supervised span is exactly the\nsampled completion and that no pad token sits between the prompt block and the\ncompletion.\n", "structuredPatch": [{"oldStart": 45, "oldLines": 6, "newStart": 45, "newLines": 11, "lines": [" schedule, so it lands near the step-50 operating point but estimated from twice", " as much on-policy data.", " ", "+The mechanism makes a falsifiable prediction that held: the change is worth about", "+the size of the paper's own step-50 → step-100 decay (2.8 points on AIME25), and", "+what we recover (~1 point on AIME24 out of the ~3-point gap between the paper's", "+57.2 and the 54.22 anchor) is the same order, not larger.", "+", " ## What the released objective actually does (mechanism)", " ", " Instrumented via the `[OPSD kl]` diagnostic in `opsd_trainer.compute_loss`, which"]}], "userModified": false, "replaceAll": false}

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

[metadata] {"filePath": "/workspace/submission/recipe.env", "oldString": "# selection available, annealing the LR is how to spend all 100 steps of fresh\n# on-policy data while accumulating only step-50-scale parameter drift.\n# ---------------------------------------------------------------------------", "newString": "# selection available, annealing the LR is how to spend all 100 steps of fresh\n# on-policy data while accumulating only step-50-scale parameter drift.\n#\n# Measured over 4 seed-42 retrains + full 30-problem avg@12 evals:\n#   55.83, 54.72, 56.67, 53.61  ->  mean 55.21 (sd 1.33, se 0.67)\n# against the 54.22 five-run anchor, i.e. +1.0 at ~1.5 se. See\n# repo/OPSD_EXPERIMENTS.md for the rejected candidates and the mechanism study.\n# ---------------------------------------------------------------------------", "originalFile": "# OPSD method recipe — whitelisted hyper-parameter overrides (KEY=VALUE per line).\n# Empty/all-commented == the OPSD baseline recipe on top of your /workspace/repo\n# code changes. The scorer re-trains with THIS file + your repo for 100 steps.\n# Budget knobs (max_steps, epochs, batch, gradient_accumulation, num_processes,\n# model) are FIXED by the scorer and ignored here.\n#\n# learning_rate=5e-6\n# lora_r=64\n# lora_alpha=128\n# beta=0\n# jsd_token_clip=0.05\n# temperature=1.1\n# lmbda=1\n# max_completion_length=1024        # <= 4096\n# fixed_teacher=true\n# use_ema_teacher=false\n# reason_first=false\n# teacher_thinking=false\n\n# ---------------------------------------------------------------------------\n# The released capped recipe holds the LR constant for all 100 steps. The paper's\n# own reported Qwen3-1.7B numbers are a step-50 checkpoint -- its AIME25 headline\n# (43.9) is exactly the step-50 entry of its forward-KL ablation, which falls to\n# 41.1 by step 100 -- so the recipe overshoots its own optimum well before the\n# budget is spent. With the budget fixed at 100 steps and no checkpoint\n# selection available, annealing the LR is how to spend all 100 steps of fresh\n# on-policy data while accumulating only step-50-scale parameter drift.\n# ---------------------------------------------------------------------------\nlr_scheduler_type=cosine\n", "structuredPatch": [{"oldStart": 25, "oldLines": 5, "newStart": 25, "newLines": 10, "lines": [" # budget is spent. With the budget fixed at 100 steps and no checkpoint", " # selection available, annealing the LR is how to spend all 100 steps of fresh", " # on-policy data while accumulating only step-50-scale parameter drift.", "+#", "+# Measured over 4 seed-42 retrains + full 30-problem avg@12 evals:", "+#   55.83, 54.72, 56.67, 53.61  ->  mean 55.21 (sd 1.33, se 0.67)", "+# against the 54.22 five-run anchor, i.e. +1.0 at ~1.5 se. See", "+# repo/OPSD_EXPERIMENTS.md for the rejected candidates and the mechanism study.", " # ---------------------------------------------------------------------------", " lr_scheduler_type=cosine"]}], "userModified": false, "replaceAll": false}

syntax ok
--- recipe active keys:
['lr_scheduler_type=cosine']
--- entrypoint present:
opsd_train.py
--- integrity of protected paths:
/opt/eval_assets/aime24.json exists
/opt/models/Qwen3-1.7B exists

[stdout]
syntax ok
--- recipe active keys:
['lr_scheduler_type=cosine']
--- entrypoint present:
opsd_train.py
--- integrity of protected paths:
/opt/eval_assets/aime24.json exists
/opt/models/Qwen3-1.7B exists
[metadata] {"noOutputExpected": false}