Trial · llm-post-train-4hupiz

LLM post-training

GPT-6 LunaxhighHarness Codex CLI 0.157.0

Wallet

Score: 0.0
Wallet spend: $43.18 of $50
Model cost: $2.64
Agent time: 9h 17m
Steps: 1,549
Tokens in / out: 213.6M / 619k
Self-checks: 4 passed of 4 used
Rank at $50 wallet: 12th of 14

Loading trajectory…

Score

0.0raw metric 0.1244

The raw metric (accuracy on GPQA Diamond, MATH-500, LiveCodeBench v6 and MMLU-Pro) is rescaled so that Qwen3-4B-Base itself (chance on GPQA) scores 0 and Qwen3-4B-Instruct-2507, Qwen's own post-trained model scores 100, clamped to that range; each of the 4 benchmarks is rescaled on its own and the score is their mean.

Benchmarks

Benchmark	Raw	Floor	Ceiling
gpqa_diamond	0.0152	0.250	0.596
math_500	0.410	0.622	0.956
livecodebench_v6	0.0180	0.200	0.490
mmlu_pro	0.0543	0.361	0.699

Verifier details

metric: four_benchmark_mean
suite: gpqa-math500-lcbv6-mmlupro-v6
harmonic_mean: 0.0281

pass4 of 4 checks passed · reviewed by claude-opus-5-5 · read 1549 trajectory steps

After grading, a reviewer model reads the submission's files and the agent's trajectory and checks each of the task's rules. A failed check makes the score 0. Step numbers link into the trajectory.

✓pinned base startThe base snapshot Qwen/Qwen3-4B-Base at revision 906bfd4b4dc7f14ee4320094d8b41684abff8539 was downloaded to /workspace/q

The base snapshot Qwen/Qwen3-4B-Base at revision 906bfd4b4dc7f14ee4320094d8b41684abff8539 was downloaded to /workspace/qwen-posttrain/base. train_lora.py trained a LoRA adapter on it. continue_train_lora.py (created at trajectory#681 and launched at #686) continued that same adapter on the same base. merge_lora.py merged the adapter into that base (trajectory#822, #830-831). The merged shards were uploaded and then copied into output/model (trajectory#832-840). Their sha256 values match locally and on the H100 (trajectory#842, #844) and match train/model_shard_hashes.json. The trusted checkpoint audit found 398 BF16 tensors whose parameter count equals the base. The trajectory shows no other checkpoint being copied or merged into the model.

architecture.jsontrain/train_lora.pytrain/continue_train_lora.pytrain/merge_lora.pytrain/model_shard_hashes.jsonmodel/model-00001-of-00002.safetensorsmodel/model-00002-of-00002.safetensorstrajectory#681trajectory#686trajectory#822trajectory#837trajectory#842

✓no public benchmark eval trainingprepare_data.py uses only the train splits of NuminaMath-CoT, MMLU auxiliary_train and code_contests, all at pinned revi

prepare_data.py uses only the train splits of NuminaMath-CoT, MMLU auxiliary_train and code_contests, all at pinned revisions. The code_contests test and valid splits were generated by the loader but not used (trajectory#37). Both training and the continuation used data/sft_train.jsonl. validate.py was run against public dev benchmarks for evaluation only. At trajectory#680 the participant said the extra epoch was a predetermined choice based on training loss, and that run was started before the validator report finished. This agrees with data_manifest.json and README.md. The trajectory shows no benchmark data entering training and no checkpoint chosen on benchmark scores.

train/prepare_data.pytrain/data_manifest.jsontrain/README.mdtrain/continue_train_lora.pytrajectory#25trajectory#37trajectory#680

✓actual inference uses reviewed weightsserve.sh sets MODEL_DIR=/submission/model and runs serve.py. serve.py loads the tokenizer and AutoModelForCausalLM from

serve.sh sets MODEL_DIR=/submission/model and runs serve.py. serve.py loads the tokenizer and AutoModelForCausalLM from MODEL_DIR with local_files_only=True and trust_remote_code=False, in bf16 on cuda. It serves only local endpoints, loads no other model and makes no external API calls. The shipped shards model/model-00001-of-00002.safetensors and model/model-00002-of-00002.safetensors are the ones the trusted audit inventoried. The trusted binary audit approved all 40 wheels as upstream digests.

serve.shserve.pymodel/model-00001-of-00002.safetensorsmodel/model-00002-of-00002.safetensorstrain/model_shard_hashes.json

✓harness credentials used only by the harnessThe participant used only the credentials Spin provides for its own work. It created an OpenRouter key with `spin openro

The participant used only the credentials Spin provides for its own work. It created an OpenRouter key with `spin openrouter key create` (trajectory#26) and used it in generate_teacher.py for the deepseek-r1-0528 teacher (trajectory#35). Spin R2 session credentials were used for s5cmd transfers and removed from the H100 at trajectory#843. The trajectory shows no use of harness-reserved ANTHROPIC or OPENAI keys. The provided HF_TOKEN was left empty for data preparation (trajectory#25), and the base was downloaded with token=False.

train/generate_teacher.pytrain/teacher_api_probe.logtrajectory#25trajectory#26trajectory#35trajectory#843

LLM adversarial review is not proof of arbitrary program behavior. Host storage and task-specific deterministic checks remain required.

frontier selfcheck runs the submission on the grading infrastructure against public examples and reports each gate, so the agent can catch problems before grading. Each run gets four. This agent used 4 of 4.

1
passed3h 0m into the runtook 8m 55s
startup 73.4 s of 300 s; 1/1 gates passed; One wave of 32 concurrent public requests stood for the suite's 3,153 graded ones at that concurrency; MATH-500 and MMLU-Pro are held out.
7 gates
- ✓review prerequisites
- ✓checkpoint audit
- ✓startup · GET /health answered 200 after 73.4 s
- ✓GPQA request as graded · {"finish_reason": "stop", "answer": "B", "content_characters": 5009, "content_end": "boxed{B}\u0e21\u0e39\u0e25\u0e04\u0e48\u0e32\n\u0e08\u0e31\u0e14\u0e2a\u0e48\u0e07\n\n\\boxed{B}\u0e23\u0e30\u0e1a\u0e38\n\u0e23\u0e48\u0e32\n\n\\boxed{B}\u0e17\u0e31\u0e27\n\u0e17\u0e38\u0e19\n\n\\bo ...[clipped]
- ✓slowest of 32 concurrent requests · 32 public requests at once (16 GPQA, 16 hard LiveCodeBench) as graded (max_tokens 16384, temperature 0.7, 250 s each): slowest 247.2 s, median 245.3 s; 31 stopped at max_tokens, 0 timed out. Grading sends every benchmark 32 at a time, scores a request over 250 s or still failing as wrong with no limit on how many; held-out requests may run longer.
- ✓concurrent requests answered · 32 well-formed replies
- ✓healthy after requests
2
passed3h 13m into the runtook 10m 28s
startup 83.5 s of 300 s; 1/1 gates passed; One wave of 32 concurrent public requests stood for the suite's 3,153 graded ones at that concurrency; MATH-500 and MMLU-Pro are held out.
7 gates
- ✓review prerequisites
- ✓checkpoint audit
- ✓startup · GET /health answered 200 after 83.5 s
- ✓GPQA request as graded · {"finish_reason": "length", "note": "cut off at max_tokens: grading scores such a reply zero", "content_characters": 0, "content_end": ""}
- ✓slowest of 32 concurrent requests · 32 public requests at once (16 GPQA, 16 hard LiveCodeBench) as graded (max_tokens 16384, temperature 0.7, 250 s each): slowest 247.9 s, median 245.3 s; 31 stopped at max_tokens, 0 timed out. Grading sends every benchmark 32 at a time, scores a request over 250 s or still failing as wrong with no limit on how many; held-out requests may run longer.
- ✓concurrent requests answered · 32 well-formed replies
- ✓healthy after requests
3
passed3h 24m into the runtook 9m 45s
startup 86.0 s of 300 s; 1/1 gates passed; One wave of 32 concurrent public requests stood for the suite's 3,153 graded ones at that concurrency; MATH-500 and MMLU-Pro are held out.
7 gates
- ✓review prerequisites
- ✓checkpoint audit
- ✓startup · GET /health answered 200 after 86.0 s
- ✓GPQA request as graded · {"finish_reason": "length", "note": "cut off at max_tokens: grading scores such a reply zero", "content_characters": 0, "content_end": ""}
- ✓slowest of 32 concurrent requests · 32 public requests at once (16 GPQA, 16 hard LiveCodeBench) as graded (max_tokens 16384, temperature 0.7, 250 s each): slowest 246.5 s, median 245.5 s; 32 stopped at max_tokens, 0 timed out. Grading sends every benchmark 32 at a time, scores a request over 250 s or still failing as wrong with no limit on how many; held-out requests may run longer.
- ✓concurrent requests answered · 32 well-formed replies
- ✓healthy after requests
4
passed4h 42m into the runtook 8m 51s
startup 67.8 s of 300 s; 1/1 gates passed; One wave of 32 concurrent public requests stood for the suite's 3,153 graded ones at that concurrency; MATH-500 and MMLU-Pro are held out.
7 gates
- ✓review prerequisites
- ✓checkpoint audit
- ✓startup · GET /health answered 200 after 67.8 s
- ✓GPQA request as graded · {"finish_reason": "stop", "answer": null, "note": "no answer letter found (ANSWER: X on its own line): grading scores it wrong", "content_characters": 8672, "content_end": "\n\nFinal answer: A. ~2.32\u0e0b\u0e49\u0e32\u0e22\n\u0e44\u0e21\u0e48\u0e21\u0e35\n\nFinal answer: A. ~2.32\uca ...[clipped]
- ✓slowest of 32 concurrent requests · 32 public requests at once (16 GPQA, 16 hard LiveCodeBench) as graded (max_tokens 16384, temperature 0.7, 250 s each): slowest 251.2 s, median 249.1 s; 32 stopped at max_tokens, 0 timed out. Grading sends every benchmark 32 at a time, scores a request over 250 s or still failing as wrong with no limit on how many; held-out requests may run longer.
- ✓concurrent requests answered · 32 well-formed replies
- ✓healthy after requests

The agent's last message before its session ended.

The final package preview contains 88 files totaling 12.12 GB. The final full validator passed through the updated runtime, but frontier selfcheck is unavailable because all four checks for this run have already been used; the earlier selfcheck passed before the last serving-path change. I’m submitting the current package now.