on-policy-self-distillation

claude-opus-5

on-policy-self-distillation — graded by the task's verifier against a measured baseline.

backend: modal
variant: claude claude-opus-5
metric: AIME24 avg@12 accuracy
actions: 148
duration: 9h 4m
recorded: 2026-08-03

run idopsd-aime24-4gpus__LSVoBuR

Everything claude-opus-5 did on this task, in order.

download JSON ↗

claude-codeclaude-opus-5145 turns21 narration148 action148 observation124 reasoning1 final

#126

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashFind lingering trainer processes

args

{
  "command": "ps -eo pid,etime,rss,args --sort=-rss | head -8 | cut -c1-140",
  "description": "Find lingering trainer processes"
}

Bash result

PID     ELAPSED   RSS COMMAND
29784       01:39 11140264 /usr/bin/python3 -u opsd_train.py --learning_rate 5e-6 --max_grad_norm 0.1 --weight_decay 0 --lr_scheduler_type c
29785       01:39 11109776 /usr/bin/python3 -u opsd_train.py --learning_rate 5e-6 --max_grad_norm 0.1 --weight_decay 0 --lr_scheduler_type c
29787       01:39 11095176 /usr/bin/python3 -u opsd_train.py --learning_rate 5e-6 --max_grad_norm 0.1 --weight_decay 0 --lr_scheduler_type c
29786       01:39 11005700 /usr/bin/python3 -u opsd_train.py --learning_rate 5e-6 --max_grad_norm 0.1 --weight_decay 0 --lr_scheduler_type c
29736       01:46 5826676 /usr/bin/python3 /usr/local/bin/accelerate launch --config_file accelerate.yaml --num_processes 4 --gradient_accum
29773       01:40 4622348 /usr/bin/python3 /usr/local/lib/python3.10/dist-packages/torch/_inductor/compile_worker/__main__.py --pickler=torc
29808       01:36 736052 /usr/bin/python3 /usr/local/lib/python3.10/dist-packages/torch/_inductor/compile_worker/__main__.py --pickler=torch

[stdout]
  PID     ELAPSED   RSS COMMAND
29784       01:39 11140264 /usr/bin/python3 -u opsd_train.py --learning_rate 5e-6 --max_grad_norm 0.1 --weight_decay 0 --lr_scheduler_type c
29785       01:39 11109776 /usr/bin/python3 -u opsd_train.py --learning_rate 5e-6 --max_grad_norm 0.1 --weight_decay 0 --lr_scheduler_type c
29787       01:39 11095176 /usr/bin/python3 -u opsd_train.py --learning_rate 5e-6 --max_grad_norm 0.1 --weight_decay 0 --lr_scheduler_type c
29786       01:39 11005700 /usr/bin/python3 -u opsd_train.py --learning_rate 5e-6 --max_grad_norm 0.1 --weight_decay 0 --lr_scheduler_type c
29736       01:46 5826676 /usr/bin/python3 /usr/local/bin/accelerate launch --config_file accelerate.yaml --num_processes 4 --gradient_accum
29773       01:40 4622348 /usr/bin/python3 /usr/local/lib/python3.10/dist-packages/torch/_inductor/compile_worker/__main__.py --pickler=torc
29808       01:36 736052 /usr/bin/python3 /usr/local/lib/python3.10/dist-packages/torch/_inductor/compile_worker/__main__.py --pickler=torch
[metadata] {"noOutputExpected": false}

#127

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashKill lingering trainers

args

{
  "command": "kill -9 29784 29785 29786 29787 29736 29773 29808 2>/dev/null; sleep 12; ps -eo pid,args --sort=-rss | grep -c \"opsd_train\\|accelerate launch\" ; nvidia-smi --query-compute-apps=pid --format=csv,noheader | wc -l",
  "description": "Kill lingering trainers"
}

Bash result

6
0

[stdout]
6
0
[metadata] {"noOutputExpected": false}

#128

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashEncode e6 method as repo defaults

args

{
  "command": "cd /workspace/repo && python - <<'EOF'\nimport re\np='opsd_train.py'; s=open(p).read()\ns=s.replace('os.environ.get(\"OPSD_DATA_FILTER\", \"none\")','os.environ.get(\"OPSD_DATA_FILTER\", \"hard_numeric\")')\ns=s.replace('os.environ.get(\"OPSD_TEACHER_REF\", \"cot\")','os.environ.get(\"OPSD_TEACHER_REF\", \"solution\")')\ns=s.replace('os.environ.get(\"OPSD_WAVG_LAST\", \"20\")','os.environ.get(\"OPSD_WAVG_LAST\", \"0\")')\nopen(p,'w').write(s)\np='opsd_trainer.py'; s=open(p).read()\ns=s.replace('_env_flag(\"OPSD_FIX_POSITION_IDS\", True)','_env_flag(\"OPSD_FIX_POSITION_IDS\", False)')\nopen(p,'w').write(s)\nEOF\ngrep -n 'OPSD_DATA_FILTER\"\\|OPSD_TEACHER_REF\"\\|OPSD_WAVG_LAST\"' opsd_train.py; grep -n 'OPSD_FIX_POSITION_IDS\\|OPSD_CLIP_MODE' opsd_trainer.py | head -3\ncat > /workspace/submission/recipe.env <<'EOF'\n# OPSD recipe overrides (whitelisted keys only, KEY=VALUE per line).\n#\n# Same 100 optimizer steps and the same *average* learning rate as the released recipe\n# (constant 5e-6), but front-loaded and annealed to zero: within this budget the run is\n# noise-dominated near the end, so spending the early steps at a higher rate and then\n# annealing lands on a better point than sitting at a constant rate the whole way.\nlearning_rate=1e-5\nlr_scheduler_type=linear\nEOF\ncat /workspace/submission/recipe.env | tail -3; cat /workspace/.timer/remaining_secs",
  "description": "Encode e6 method as repo defaults"
}

Bash result

311:    data_filter = os.environ.get("OPSD_DATA_FILTER", "hard_numeric")
337:    TEACHER_REF = os.environ.get("OPSD_TEACHER_REF", "solution")
389:    wavg_last = int(os.environ.get("OPSD_WAVG_LAST", "0"))
94:CLIP_MODE = os.environ.get("OPSD_CLIP_MODE", "element")
101:FIX_POSITION_IDS = _env_flag("OPSD_FIX_POSITION_IDS", False)
# annealing lands on a better point than sitting at a constant rate the whole way.
learning_rate=1e-5
lr_scheduler_type=linear
7115

[stdout]
311:    data_filter = os.environ.get("OPSD_DATA_FILTER", "hard_numeric")
337:    TEACHER_REF = os.environ.get("OPSD_TEACHER_REF", "solution")
389:    wavg_last = int(os.environ.get("OPSD_WAVG_LAST", "0"))
94:CLIP_MODE = os.environ.get("OPSD_CLIP_MODE", "element")
101:FIX_POSITION_IDS = _env_flag("OPSD_FIX_POSITION_IDS", False)
# annealing lands on a better point than sitting at a constant rate the whole way.
learning_rate=1e-5
lr_scheduler_type=linear
7115
[metadata] {"noOutputExpected": false}

#129

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashLaunch two confirmation cycles

args

{
  "command": "cd /workspace && nvidia-smi --query-compute-apps=pid --format=csv,noheader | wc -l && cat > /workspace/confirm.sh <<'EOF'\n#!/bin/bash\n# Confirmation runs of the submission path: repo defaults + recipe.env, NO env overrides.\nNAME=e6r1 bash /workspace/cycle.sh\nNAME=e6r2 bash /workspace/cycle.sh\nEOF\nnohup bash /workspace/confirm.sh > /workspace/runs/confirm.log 2>&1 &\nsleep 45; cat /workspace/runs/confirm.log; grep -n 'filter=hard_numeric\\|learning_rate' /workspace/runs/e6r1_train.out | head",
  "description": "Launch two confirmation cycles"
}

Bash result

0
=== [e6r1] train start 07:51:45  (remaining 6985s)
65:[data] filter=hard_numeric: 29434 -> 9775 examples
71:[data] filter=hard_numeric: 29434 -> 9775 examples
81:[data] filter=hard_numeric: 29434 -> 9775 examples
88:[data] filter=hard_numeric: 29434 -> 9775 examples

[stdout]
0
=== [e6r1] train start 07:51:45  (remaining 6985s)
65:[data] filter=hard_numeric: 29434 -> 9775 examples
71:[data] filter=hard_numeric: 29434 -> 9775 examples
81:[data] filter=hard_numeric: 29434 -> 9775 examples
88:[data] filter=hard_numeric: 29434 -> 9775 examples
[metadata] {"noOutputExpected": false}

#130

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashPoll e6r1 training progress

args

{
  "command": "sleep 560; tail -3 runs/e6r1_train.out | cut -c1-300; grep -c 'loss' runs/e6r1_train.out",
  "description": "Poll e6r1 training progress",
  "timeout": 580000
}

Bash result

================================================================================


 61%|██████    | 61/100 [09:10<05:50,  8.98s/it]
30

[stdout]
================================================================================


 61%|██████    | 61/100 [09:10<05:50,  8.98s/it]
30
[metadata] {"noOutputExpected": false}

#131

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashCheck LR schedule in log

args

{
  "command": "grep -o \"'learning_rate': [0-9.e-]*\" runs/e6r1_train.out | head -3 && grep -o \"'learning_rate': [0-9.e-]*\" runs/e6r1_train.out | tail -2",
  "description": "Check LR schedule in log"
}

Bash result

'learning_rate': 9.9e-06
'learning_rate': 9.7e-06
'learning_rate': 9.5e-06
'learning_rate': 4.3e-06
'learning_rate': 4.1e-06

[stdout]
'learning_rate': 9.9e-06
'learning_rate': 9.7e-06
'learning_rate': 9.5e-06
'learning_rate': 4.3e-06
'learning_rate': 4.1e-06
[metadata] {"noOutputExpected": false}

#132

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashPoll e6r1 status

args

{
  "command": "sleep 580; cd /workspace; cat runs/confirm.log; tail -2 runs/e6r1_eval.out 2>/dev/null | cut -c1-200",
  "description": "Poll e6r1 status",
  "timeout": 600000
}

Bash result

=== [e6r1] train start 07:51:45  (remaining 6985s)
=== [e6r1] train rc=0 08:07:57
/workspace/runs/e6r1/capped_seed42/checkpoint-100
=== [e6r1] eval start 08:07:57

Processed prompts:   0%|          | 0/84 [00:00<?, ?it/s, est. speed input: 0.00 toks/s, output: 0.00 toks/s]
Processed prompts:   0%|          | 0/84 [00:00<?, ?it/s, est. speed input: 0.00 toks/s, 

Processed prompts:   0%|          | 0/96 [00:00<?, ?it/s, est. speed input: 0.00 toks/s, output: 0.00 toks/s]

[stdout]
=== [e6r1] train start 07:51:45  (remaining 6985s)
=== [e6r1] train rc=0 08:07:57
/workspace/runs/e6r1/capped_seed42/checkpoint-100
=== [e6r1] eval start 08:07:57

Processed prompts:   0%|          | 0/84 [00:00<?, ?it/s, est. speed input: 0.00 toks/s, output: 0.00 toks/s]
Processed prompts:   0%|          | 0/84 [00:00<?, ?it/s, est. speed input: 0.00 toks/s, 

Processed prompts:   0%|          | 0/96 [00:00<?, ?it/s, est. speed input: 0.00 toks/s, output: 0.00 toks/s]
[metadata] {"noOutputExpected": false}

#133

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashPoll e6r1 eval

args

{
  "command": "sleep 585; cd /workspace; cat runs/confirm.log; cat .timer/remaining_secs",
  "description": "Poll e6r1 eval",
  "timeout": 600000
}

Bash result

=== [e6r1] train start 07:51:45  (remaining 6985s)
=== [e6r1] train rc=0 08:07:57
/workspace/runs/e6r1/capped_seed42/checkpoint-100
=== [e6r1] eval start 08:07:57
5201

[stdout]
=== [e6r1] train start 07:51:45  (remaining 6985s)
=== [e6r1] train rc=0 08:07:57
/workspace/runs/e6r1/capped_seed42/checkpoint-100
=== [e6r1] eval start 08:07:57
5201
[metadata] {"noOutputExpected": false}

#134

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashPoll e6r1 eval

args

{
  "command": "sleep 585; cd /workspace; cat runs/confirm.log; cat .timer/remaining_secs",
  "description": "Poll e6r1 eval",
  "timeout": 600000
}

Bash result

=== [e6r1] train start 07:51:45  (remaining 6985s)
=== [e6r1] train rc=0 08:07:57
/workspace/runs/e6r1/capped_seed42/checkpoint-100
=== [e6r1] eval start 08:07:57
4610

[stdout]
=== [e6r1] train start 07:51:45  (remaining 6985s)
=== [e6r1] train rc=0 08:07:57
/workspace/runs/e6r1/capped_seed42/checkpoint-100
=== [e6r1] eval start 08:07:57
4610
[metadata] {"noOutputExpected": false}

#135

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashPoll for e6r1 result

args

{
  "command": "sleep 420; cd /workspace; cat runs/confirm.log; cat .timer/remaining_secs",
  "description": "Poll for e6r1 result",
  "timeout": 440000
}

Bash result

=== [e6r1] train start 07:51:45  (remaining 6985s)
=== [e6r1] train rc=0 08:07:57
/workspace/runs/e6r1/capped_seed42/checkpoint-100
=== [e6r1] eval start 08:07:57
=== [e6r1] eval rc=0 08:33:11
RESULT e6r1: avg@12=53.06 pass@12=76.67 fmt=98.6
=== [e6r1] done 08:33:11 (remaining 4500s)
=== [e6r2] train start 08:33:11  (remaining 4500s)
4180

[stdout]
=== [e6r1] train start 07:51:45  (remaining 6985s)
=== [e6r1] train rc=0 08:07:57
/workspace/runs/e6r1/capped_seed42/checkpoint-100
=== [e6r1] eval start 08:07:57
=== [e6r1] eval rc=0 08:33:11
RESULT e6r1: avg@12=53.06 pass@12=76.67 fmt=98.6
=== [e6r1] done 08:33:11 (remaining 4500s)
=== [e6r2] train start 08:33:11  (remaining 4500s)
4180
[metadata] {"noOutputExpected": false}

#136

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashAudit all env-var defaults in repo

args

{
  "command": "cd /workspace/repo && grep -n 'os.environ.get\\|_env_flag(' opsd_trainer.py opsd_train.py | grep -v setdefault",
  "description": "Audit all env-var defaults in repo"
}

Bash result

opsd_train.py:162:    num_processes = int(os.environ.get("WORLD_SIZE", 1))
opsd_train.py:211:    if os.environ.get("LOCAL_RANK", "0") == "0":
opsd_train.py:311:    data_filter = os.environ.get("OPSD_DATA_FILTER", "hard_numeric")
opsd_train.py:337:    TEACHER_REF = os.environ.get("OPSD_TEACHER_REF", "solution")
opsd_train.py:338:    TEACHER_REF_MAX_CHARS = int(os.environ.get("OPSD_TEACHER_REF_MAX_CHARS", "8000"))
opsd_train.py:389:    wavg_last = int(os.environ.get("OPSD_WAVG_LAST", "0"))
opsd_trainer.py:88:def _env_flag(name, default):
opsd_trainer.py:89:    return os.environ.get(name, "1" if default else "0").lower() in ("1", "true", "yes")
opsd_trainer.py:94:CLIP_MODE = os.environ.get("OPSD_CLIP_MODE", "element")
opsd_trainer.py:101:FIX_POSITION_IDS = _env_flag("OPSD_FIX_POSITION_IDS", False)
opsd_trainer.py:105:LOSS_TEMPERATURE = float(os.environ.get("OPSD_LOSS_TEMPERATURE", "0") or 0)

[stdout]
opsd_train.py:162:    num_processes = int(os.environ.get("WORLD_SIZE", 1))
opsd_train.py:211:    if os.environ.get("LOCAL_RANK", "0") == "0":
opsd_train.py:311:    data_filter = os.environ.get("OPSD_DATA_FILTER", "hard_numeric")
opsd_train.py:337:    TEACHER_REF = os.environ.get("OPSD_TEACHER_REF", "solution")
opsd_train.py:338:    TEACHER_REF_MAX_CHARS = int(os.environ.get("OPSD_TEACHER_REF_MAX_CHARS", "8000"))
opsd_train.py:389:    wavg_last = int(os.environ.get("OPSD_WAVG_LAST", "0"))
opsd_trainer.py:88:def _env_flag(name, default):
opsd_trainer.py:89:    return os.environ.get(name, "1" if default else "0").lower() in ("1", "true", "yes")
opsd_trainer.py:94:CLIP_MODE = os.environ.get("OPSD_CLIP_MODE", "element")
opsd_trainer.py:101:FIX_POSITION_IDS = _env_flag("OPSD_FIX_POSITION_IDS", False)
opsd_trainer.py:105:LOSS_TEMPERATURE = float(os.environ.get("OPSD_LOSS_TEMPERATURE", "0") or 0)
[metadata] {"noOutputExpected": false}

#137

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashVerify loss temperature off and thinking flags

args

{
  "command": "grep -n 'LOSS_TEMPERATURE' opsd_trainer.py && grep -n 'thinking' opsd_train.py | sed -n '1,40p'",
  "description": "Verify loss temperature off and thinking flags"
}

Bash result

105:LOSS_TEMPERATURE = float(os.environ.get("OPSD_LOSS_TEMPERATURE", "0") or 0)
215:        self.loss_temperature = LOSS_TEMPERATURE or args.temperature
135:    student_thinking: bool = field(
138:            "help": "Whether to enable Qwen3 thinking mode for the student during rollout. "
142:    teacher_thinking: bool = field(
145:            "help": "Whether to enable Qwen3 thinking mode for the teacher when scoring student tokens. "
146:            "Default True. Set to False for the matched non-thinking ablation (both nonthink)."
363:    teacher_thinking = script_args.teacher_thinking
365:        teacher_thinking = os.environ["OPSD_TEACHER_THINKING"].lower() in ("1", "true", "yes")
366:    student_thinking = script_args.student_thinking
368:        student_thinking = os.environ["OPSD_STUDENT_THINKING"].lower() in ("1", "true", "yes")
369:    print(f"[modes] teacher_thinking={teacher_thinking} student_thinking={student_thinking}")
378:        use_thinking_machines_loss=script_args.use_tinker_loss,
385:        student_thinking=student_thinking,
386:        teacher_thinking=teacher_thinking,

[stdout]
105:LOSS_TEMPERATURE = float(os.environ.get("OPSD_LOSS_TEMPERATURE", "0") or 0)
215:        self.loss_temperature = LOSS_TEMPERATURE or args.temperature
135:    student_thinking: bool = field(
138:            "help": "Whether to enable Qwen3 thinking mode for the student during rollout. "
142:    teacher_thinking: bool = field(
145:            "help": "Whether to enable Qwen3 thinking mode for the teacher when scoring student tokens. "
146:            "Default True. Set to False for the matched non-thinking ablation (both nonthink)."
363:    teacher_thinking = script_args.teacher_thinking
365:        teacher_thinking = os.environ["OPSD_TEACHER_THINKING"].lower() in ("1", "true", "yes")
366:    student_thinking = script_args.student_thinking
368:        student_thinking = os.environ["OPSD_STUDENT_THINKING"].lower() in ("1", "true", "yes")
369:    print(f"[modes] teacher_thinking={teacher_thinking} student_thinking={student_thinking}")
378:        use_thinking_machines_loss=script_args.use_tinker_loss,
385:        student_thinking=student_thinking,
386:        teacher_thinking=teacher_thinking,
[metadata] {"noOutputExpected": false}

#138

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashPoll e6r2 training

args

{
  "command": "cd /workspace && grep -h '\\[modes\\]' runs/e6r1_train.out | head -1; sleep 570; cat runs/confirm.log | tail -4; cat .timer/remaining_secs",
  "description": "Poll e6r2 training",
  "timeout": 590000
}

Bash result

[modes] teacher_thinking=True student_thinking=False
=== [e6r1] eval rc=0 08:33:11
RESULT e6r1: avg@12=53.06 pass@12=76.67 fmt=98.6
=== [e6r1] done 08:33:11 (remaining 4500s)
=== [e6r2] train start 08:33:11  (remaining 4500s)
3579

[stdout]
[modes] teacher_thinking=True student_thinking=False
=== [e6r1] eval rc=0 08:33:11
RESULT e6r1: avg@12=53.06 pass@12=76.67 fmt=98.6
=== [e6r1] done 08:33:11 (remaining 4500s)
=== [e6r2] train start 08:33:11  (remaining 4500s)
3579
[metadata] {"noOutputExpected": false}

#139

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashPoll e6r2

args

{
  "command": "sleep 585; cd /workspace; tail -3 runs/confirm.log; cat .timer/remaining_secs",
  "description": "Poll e6r2",
  "timeout": 600000
}

Bash result

=== [e6r2] train rc=0 08:49:23
/workspace/runs/e6r2/capped_seed42/checkpoint-100
=== [e6r2] eval start 08:49:23
2988

[stdout]
=== [e6r2] train rc=0 08:49:23
/workspace/runs/e6r2/capped_seed42/checkpoint-100
=== [e6r2] eval start 08:49:23
2988
[metadata] {"noOutputExpected": false}

#140

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashPoll e6r2 eval

args

{
  "command": "sleep 590; cd /workspace; tail -3 runs/confirm.log; cat .timer/remaining_secs",
  "description": "Poll e6r2 eval",
  "timeout": 600000
}

Bash result

=== [e6r2] train rc=0 08:49:23
/workspace/runs/e6r2/capped_seed42/checkpoint-100
=== [e6r2] eval start 08:49:23
2397

[stdout]
=== [e6r2] train rc=0 08:49:23
/workspace/runs/e6r2/capped_seed42/checkpoint-100
=== [e6r2] eval start 08:49:23
2397
[metadata] {"noOutputExpected": false}

#141

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashPoll e6r2 eval result

args

{
  "command": "sleep 590; cd /workspace; tail -4 runs/confirm.log; cat .timer/remaining_secs",
  "description": "Poll e6r2 eval result",
  "timeout": 600000
}

Bash result

=== [e6r2] eval start 08:49:23
=== [e6r2] eval rc=0 09:15:15
RESULT e6r2: avg@12=56.11 pass@12=80.00 fmt=99.2
=== [e6r2] done 09:15:15 (remaining 1976s)
1796

[stdout]
=== [e6r2] eval start 08:49:23
=== [e6r2] eval rc=0 09:15:15
RESULT e6r2: avg@12=56.11 pass@12=80.00 fmt=99.2
=== [e6r2] done 09:15:15 (remaining 1976s)
1796
[metadata] {"noOutputExpected": false}

#142

narration

Three runs of the exact submission path are now in: 56.94, 53.06, 56.11 → mean 55.37 (anchor 54.22). Finalizing.

toolBashFinal integrity and submission check

args

{
  "command": "ls -la /opt/eval_assets/aime24.json && nvidia-smi --query-compute-apps=pid --format=csv,noheader | wc -l && echo \"--- recipe.env ---\" && cat submission/recipe.env && echo \"--- method lines ---\" && sed -n '305,335p' repo/opsd_train.py",
  "description": "Final integrity and submission check"
}

Bash result

-r--r--r-- 1 root root 11858 Aug  2 17:09 /opt/eval_assets/aime24.json
0
--- recipe.env ---
# OPSD recipe overrides (whitelisted keys only, KEY=VALUE per line).
#
# Same 100 optimizer steps and the same *average* learning rate as the released recipe
# (constant 5e-6), but front-loaded and annealed to zero: within this budget the run is
# noise-dominated near the end, so spending the early steps at a higher rate and then
# annealing lands on a better point than sitting at a constant rate the whole way.
learning_rate=1e-5
lr_scheduler_type=linear
--- method lines ---

    dataset = load_dataset("siyanzhao/Openthoughts_math_30k_opsd")
    train_dataset = dataset["train"]

    # ---- optional target-aligned data selection (see README table: AIME-style,
    # short-numeric-answer competition problems) ----
    data_filter = os.environ.get("OPSD_DATA_FILTER", "hard_numeric")
    if data_filter != "none":
        import re as _re

        n_before = len(train_dataset)
        if data_filter == "numeric":
            keep = lambda ex: bool(_re.fullmatch(r"-?\d{1,6}", (ex["Answer"] or "").strip()))
        elif data_filter == "hard_numeric":
            keep = lambda ex: (
                bool(_re.fullmatch(r"-?\d{1,6}", (ex["Answer"] or "").strip()))
                and ex["generated_token_count"] >= 2000
            )
        else:
            raise ValueError(f"unknown OPSD_DATA_FILTER={data_filter}")
        train_dataset = train_dataset.filter(keep)
        print(f"[data] filter={data_filter}: {n_before} -> {len(train_dataset)} examples")

    # ---- privileged context for the teacher -------------------------------------------
    # OPSD conditions the teacher on the dataset's terse formal write-up ("solution").
    # That context reveals the answer but shows no *process*: the teacher therefore scores
    # the student's exploratory rollout from a "state the result" posture, and the student
    # inherits that posture. Every example here also ships the reference *reasoning trace*
    # for the same problem ("COT_Reason"): a first-person derivation that tries things,
    # checks them and backtracks before boxing the same ground-truth answer. Conditioning
    # the teacher on the trace keeps the privileged information identical while making the

[stdout]
-r--r--r-- 1 root root 11858 Aug  2 17:09 /opt/eval_assets/aime24.json
0
--- recipe.env ---
# OPSD recipe overrides (whitelisted keys only, KEY=VALUE per line).
#
# Same 100 optimizer steps and the same *average* learning rate as the released recipe
# (constant 5e-6), but front-loaded and annealed to zero: within this budget the run is
# noise-dominated near the end, so spending the early steps at a higher rate and then
# annealing lands on a better point than sitting at a constant rate the whole way.
learning_rate=1e-5
lr_scheduler_type=linear
--- method lines ---

    dataset = load_dataset("siyanzhao/Openthoughts_math_30k_opsd")
    train_dataset = dataset["train"]

    # ---- optional target-aligned data selection (see README table: AIME-style,
    # short-numeric-answer competition problems) ----
    data_filter = os.environ.get("OPSD_DATA_FILTER", "hard_numeric")
    if data_filter != "none":
        import re as _re

        n_before = len(train_dataset)
        if data_filter == "numeric":
            keep = lambda ex: bool(_re.fullmatch(r"-?\d{1,6}", (ex["Answer"] or "").strip()))
        elif data_filter == "hard_numeric":
            keep = lambda ex: (
                bool(_re.fullmatch(r"-?\d{1,6}", (ex["Answer"] or "").strip()))
                and ex["generated_token_count"] >= 2000
            )
        else:
            raise ValueError(f"unknown OPSD_DATA_FILTER={data_filter}")
        train_dataset = train_dataset.filter(keep)
        print(f"[data] filter={data_filter}: {n_before} -> {len(train_dataset)} examples")

    # ---- privileged context for the teacher -------------------------------------------
    # OPSD conditions the teacher on the dataset's terse formal write-up ("solution").
    # That context reveals the answer but shows no *process*: the teacher therefore scores
    # the student's exploratory rollout from a "state the result" posture, and the student
    # inherits that posture. Every example here also ships the reference *reasoning trace*
    # for the same problem ("COT_Reason"): a first-person derivation that tries things,
    # checks them and backtracks before boxing the same ground-truth answer. Conditioning
    # the teacher on the trace keeps the privileged information identical while making the
[metadata] {"noOutputExpected": false}

#143

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashCheck existing memory index

args

{
  "command": "cd /logs/agent/sessions/projects/-workspace/memory 2>/dev/null && ls && cat MEMORY.md 2>/dev/null | head -20",
  "description": "Check existing memory index"
}

Bash result · error

Exit code 1

[error] tool reported failure

#144

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolWrite

args

{
  "file_path": "/logs/agent/sessions/projects/-workspace/memory/opsd-aime24-100step-findings.md",
  "content": "---\nname: opsd-aime24-100step-findings\ndescription: Measured results from tuning OPSD (Qwen3-1.7B, 100-step cap) for AIME24 avg@12 — what helped and what backfired\nmetadata:\n  type: project\n---\n\nTask (2026-08-03): improve OPSD in `/workspace/repo` within a fixed 100 optimizer steps; scored on AIME24 avg@12, baseline anchor 54.22 (per-run σ≈1.48).\n\nMeasured, full 30-problem/avg@12 dev evals at seed 42:\n- **Helped:** target-aligned data selection (`Answer` matches `-?\\d{1,6}` AND `generated_token_count ≥ 2000`; 29434→9775 rows) + `learning_rate=1e-5` with `lr_scheduler_type=linear` (same *average* LR as the released constant 5e-6, front-loaded then annealed). Three runs: 56.94 / 53.06 / 56.11, mean 55.37.\n- **Backfired:** making the `jsd_token_clip=0.05` clip direction-preserving instead of a raw `clamp(max=)` (−6.7); thinking-mode student rollouts (−8.4). Roughly neutral: matched non-thinking teacher (53.33), CoT-trace privileged context (53.61), right-pad position-id fix.\n\n**Why:** the paper's loss/teacher operating point is sharply tuned — \"obvious bug fixes\" in the loss lose several points, while data selection and the LR shape are the levers that actually move it.\n\n**How to apply:** on this repo, don't touch the clip/rollout mode; tune data and LR schedule. And never trust a single dev run — a 3.9-point spread appeared across three identical-config runs.\n"
}

Write result

File created successfully at: /logs/agent/sessions/projects/-workspace/memory/opsd-aime24-100step-findings.md

[metadata] {"type": "create", "filePath": "/logs/agent/sessions/projects/-workspace/memory/opsd-aime24-100step-findings.md", "content": "---\nname: opsd-aime24-100step-findings\ndescription: \"Measured results from tuning OPSD (Qwen3-1.7B, 100-step cap) for AIME24 avg@12 — what helped and what backfired\"\nmetadata: \n  node_type: memory\n  type: project\n  originSessionId: 3476b637-41e1-4437-8c9a-f53302e59a44\n  modified: 2026-08-03T09:18:42.058Z\n---\n\nTask (2026-08-03): improve OPSD in `/workspace/repo` within a fixed 100 optimizer steps; scored on AIME24 avg@12, baseline anchor 54.22 (per-run σ≈1.48).\n\nMeasured, full 30-problem/avg@12 dev evals at seed 42:\n- **Helped:** target-aligned data selection (`Answer` matches `-?\\d{1,6}` AND `generated_token_count ≥ 2000`; 29434→9775 rows) + `learning_rate=1e-5` with `lr_scheduler_type=linear` (same *average* LR as the released constant 5e-6, front-loaded then annealed). Three runs: 56.94 / 53.06 / 56.11, mean 55.37.\n- **Backfired:** making the `jsd_token_clip=0.05` clip direction-preserving instead of a raw `clamp(max=)` (−6.7); thinking-mode student rollouts (−8.4). Roughly neutral: matched non-thinking teacher (53.33), CoT-trace privileged context (53.61), right-pad position-id fix.\n\n**Why:** the paper's loss/teacher operating point is sharply tuned — \"obvious bug fixes\" in the loss lose several points, while data selection and the LR shape are the levers that actually move it.\n\n**How to apply:** on this repo, don't touch the clip/rollout mode; tune data and LR schedule. And never trust a single dev run — a 3.9-point spread appeared across three identical-config runs.\n", "structuredPatch": [], "originalFile": null, "userModified": false, "memdirStamped": true}

#145

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolWrite

args

{
  "file_path": "/logs/agent/sessions/projects/-workspace/memory/MEMORY.md",
  "content": "- [OPSD AIME24 100-step findings](opsd-aime24-100step-findings.md) — what moved avg@12 (data selection + annealed LR) and what lost 6–8 points (loss/rollout changes).\n"
}

Write result

File created successfully at: /logs/agent/sessions/projects/-workspace/memory/MEMORY.md (file state is current in your context — no need to Read it back)

[metadata] {"type": "create", "filePath": "/logs/agent/sessions/projects/-workspace/memory/MEMORY.md", "content": "- [OPSD AIME24 100-step findings](opsd-aime24-100step-findings.md) — what moved avg@12 (data selection + annealed LR) and what lost 6–8 points (loss/rollout changes).\n", "structuredPatch": [], "originalFile": null, "userModified": false}

final summary

Done. The submission is final.

Method: target-aligned data selection + equal-average annealed LR

Two changes, both inside the fixed 100-step / batch-32 budget:

1. Data selection (in `/workspace/repo/opsd_train.py:311`). OPSD trains on all 29,434 OpenThoughts-math rows, but 100×32 = 3,200 samples is only ~11% of one epoch — so which 3,200 problems the student rolls out on is the whole training signal. I filter to problems that look like the target distribution: short integer answers (-?\d{1,6}) and a reference trace of ≥2,000 tokens (i.e. genuinely hard). 29,434 → 9,775 rows. No external data; a pure in-code filter of the scorer's dataset.

2. LR shape (`/workspace/submission/recipe.env`). learning_rate=1e-5, lr_scheduler_type=linear — the average LR is identical to the released constant 5e-6, just front-loaded and annealed to zero, so it isn't a "train more" workaround.

Results (full 30-problem avg@12, seed 42)

run	avg@12
anchor (paper recipe, 5 runs)	54.22 (σ≈1.48/run)
candidate run 1	56.94
candidate run 2	53.06
candidate run 3	56.11
candidate mean (3 runs)	55.37

+1.15 over the anchor, ≈1.0–1.4 SE on three runs — a real but modest edge, not the +2.7 the first run suggested. The 3.9-point spread across three identical configs is exactly the noise you warned about, which is why I spent the remaining budget on repeats rather than a fourth idea.

Ablations that ruled out the tempting alternatives (all full evals): direction-preserving jsd_token_clip −6.7, thinking-mode student rollouts −8.4, matched non-thinking teacher 53.33, CoT-trace privileged context 53.61, right-pad position-id fix neutral (reverted to paper behaviour), max_completion_length=2048 OOMs on 4×H100. The paper's loss/teacher operating point is sharply tuned; the headroom is in the data and the LR shape.

Not validated, so shipped off: a tail-weight-averaging callback (OPSD_WAVG_LAST, default 0) is present and unit-tested but never activates — its dev run was cut to buy the confirmation repeats.

opsd_train.py remains the entrypoint, train_capped.sh is untouched, the method needs no environment variables (repo defaults + recipe.env reproduce all three runs above), and /opt/eval_assets, /opt/models, /opt/hf_cache are unmodified. GPUs are idle; ~28 min of budget left.