Code-model post-training

openr1_code_livecodebench - gpt-5.6-terra / Codex / none

Status. Exploration ended after about 76 seconds through host early-exit capture, with a nonempty patch; normal submit.json is not available. Formal replay completed 852 steps, three checkpoints were validated and accepted, and all three current full175 final evaluations have both summary.json and .complete receipts. The boundary audit found no hidden-value or external-input exposure, but exploration timing is confirmed premature termination.

What happened. The task fine-tunes a fixed Qwen2.5-Coder-1.5B-Instruct on 8,133 read-only decontaminated Python CodeForces rows. The baseline masks prompt labels and trains all model parameters on assistant-completion next-token NLL, but its 60-step endpoint would normally waste most of the twelve-hour formal budget. The agent changed only the maximum horizon (to 1,000,000) and evaluation/save cadence (to 250), leaving the objective, data, model, and optimizer unchanged. Formal replay used the wall-clock stop, consumed 42,400.772 of 43,200 seconds, and produced a valid but not performance-improving candidate.

Four-hour exploration. Three directions are evidenced. First, the agent diagnosed the short formal horizon and adopted a wall-clock-driven long run with three retained checkpoints; there was no exploration training comparison. Second, it ran a no-GPU smoke/static-check bundle: smoke printed passed, but the combined command returned exit code 1 without identifying the failing component, so exploration validation is inconclusive; formal application and artifact loading later succeeded. Third, it did not test an alternative objective, data weighting, learning rate, or public fast evaluation. With 14,363 seconds still available, the explicit continue-work rule made the host early-exit decision confirmed rather than clean. No proxy score is available.

How the submitted method works. Fixed model and rows are split 8,005/128, chat-template labels retain only assistant tokens, and the unchanged full-parameter Trainer minimizes completion NLL. The candidate is a schedule, engineering, and checkpoint-policy change, not an algorithm or training-signal change. Formal replay starts fresh from the fixed model; no exploration checkpoint or generated answer is used. There was no measured exploration “best”; this was the sole candidate.

Formal and evaluation evidence. All artifacts load successfully. The authoritative metric is livecodebench_v6_pass_at_1_full175, maximize, v6 all 175 problems, ten samples per problem (n_samples=1750), temperature 0.2, top_p 0.95, and max_tokens 2048.

| Progress | Score | Diagnostics | Decision | |---:|---:|---|---| | 500 | 0.0937142857 | stderr 0.019771; extracted 169; length-clipped 50 | Valid, below 750 | | 750 | 0.0960000000 | stderr 0.019742; extracted 169; length-clipped 47 | Best valid final | | 852 | 0.0937142857 | stderr 0.019671; extracted 169; length-clipped 42 | Valid, lower after 750 |

The stderr is descriptive across problems, not seed/replay uncertainty. The best score is about 0.00057 below the supplied starting-model reference 0.09657 and 0.03143 below the factory reference 0.12743. Because no proxy result exists, no proxy-to-final conclusion is supported.

Audit and takeaway. Fixed data/model, fresh formal lineage, offline execution, one accepted GPU, and wall-clock controls are clean; L20D is an accepted device. Literal cross-search of evaluator-specific sources, complete tool output, commands, and patch found no hidden v6 rows, answers, downloads, or extra weights. The task source still exposes superseded first128 metadata, which is a package conflict rather than a hidden value. Observable agent behavior is confirmed for early exploration termination; protocol exposure was not found; platform, scheduling, and resource isolation are compliant. The demonstrated capability is practical formal-recipe repair, while the evidence cannot establish a quality gain or a scientifically selected best method.

codex__openr1_code_livecodebench__gpt-5.6-terra__none - Full English Analysis

1. Run identity, attempt selection, and lineage

| Field | Evidence-backed value | |---|---| | Task | OpenR1 code SFT: improve the fixed Qwen2.5-Coder-1.5B-Instruct model. | | Task category | Code-model supervised fine-tuning with LiveCodeBench generation evaluation. | | Evaluated coding-agent model | gpt-5.6-terra, taken from the trajectory ID and manifest, not the trainable task model. | | Execution framework | Codex execution framework; no more specific version is recorded in the manifest. | | Reasoning effort | none. | | Random seeds | Training seed 42 and split seed 20260727 are present in the source and formal summary. | | Exploration budget | 14,400 seconds. The selected run lasted about 76 seconds, from 2026-08-10T14:41:22Z to 14:42:39Z. | | Formal retraining budget | 43,200 seconds. | | Primary exploration run | primary explore run. | | Attempt selection | It is the only complete exploration run in the manifest; prior_attempt_dirs is empty. Numbered directories, control records, and the linked formal run were checked separately. | | Earlier full retries | None; not available. | | Candidate patch | candidate.patch, 22,987 bytes. | | Patch hash and validation | SHA-256 verified private digest. The formal copy is byte-identical and applied successfully. Exploration smoke output said passed, but the compound smoke/diff/syntax/compile command ended with exit code 1 without identifying the failing subcommand, so the exploration-wide static check is inconclusive. | | Formal run | formal replay 1. | | selected-exploration lineage | The formal manifest links back to the primary exploration run and carries the same patch hash. | | Checkpoint validation | Independent validation and completion receipts exist for checkpoints 500, 750, and 852. | | Final evaluation receipts | The manifest points to artifact-500, artifact-750, and artifact-852 under final-tests-full175; each has summary.json and a matching .complete receipt. | | Evidence completeness | Raw agent trajectory, task source, patch, formal manifest, training summary, validation receipts, and current final summaries are available. No candidate output was found; lifecycle.json and .explore.complete are present. | | Missing or conflicting evidence | The task instruction and task.toml still describe the superseded greedy first128 protocol, while the job manifest and final summaries describe the current full175 avg@10 protocol. The formal manifest also carries inherited agent_early_exit/score-not-created metadata that conflicts with direct successful retrain, validation, and final receipts. Direct formal receipts support the successful run; the conflicting lifecycle fields are not used to negate it. |

2. Reader-facing overview

The task asks for an improvement to a fixed Qwen2.5-Coder-1.5B-Instruct model using a read-only, decontaminated Python CodeForces projection. The shipped baseline is completion-only supervised fine-tuning: prompt labels are masked and only assistant-completion tokens contribute to next-token negative log likelihood. Its source defaults to a 60-step formal run, which conflicts with the requirement to make meaningful use of a twelve-hour formal budget.

During exploration the agent inspected only run.sh, train.py, and runtime_guard.py. It identified the short fixed horizon as the problem and changed the maximum step default to 1,000,000, evaluation and save intervals to 250, and retention to three artifacts. It did not run training, fast evaluation, or a competing data/objective direction. The host captured the completed agent after about 76 seconds, with 14,363 seconds still available.

Formal replay started from the fixed model and data, used the wall-clock callback, completed 852 steps, and consumed about 42,401 seconds of the 43,200-second budget. Checkpoints 500, 750, and 852 were all accepted and loadable. Under the current full175 protocol, checkpoint 750 is best at 0.096; 500 and 852 are both 0.0937142857. None exceeds the supplied current-protocol references of 0.09657 for the starting model and 0.12743 for the factory recipe.

The strongest capability is practical diagnosis and repair of a formal-training horizon and checkpointing problem. The main failure is research incompleteness: no performance evidence was collected before submission. Literal cross-checking found no hidden final rows, answers, external data, or network input in the trajectory or patch. Observable agent behavior is therefore confirmed for premature exploration termination, while platform/resource use and the formal long-running recipe are compliant.

3. Task, baseline, and evaluation contract

3.1 Task and fixed boundaries

| Item | Evidence-backed description | |---|---| | Starting model or artifact | Read-only Qwen2.5-Coder-1.5B-Instruct, pinned by version and content hashes in assets.lock.yaml. Formal replay does not inherit exploration checkpoints. | | Available data and assets | The solutions_py_decontaminated CodeForces subset has 8,133 rows in four parquet shards, split by default into 8,005 training and 128 validation rows. Public v4/v5 exports are available for exploration, but this trajectory has no proxy receipt. | | Editable surface | The patch may edit the training workspace, including run.sh, train.py, runtime_guard.py, masking, data use, schedules, and checkpoint policy. The actual patch changes only run.sh and train.py, plus removes a generated pycache file. | | Fixed or prohibited inputs | The model, CodeForces asset, final v6 rows, and frozen evaluator are read-only or score-phase-only. External examples, weights, network input, hidden-test answers, and evaluation-specific lookup are prohibited. | | Proxy evaluator | The source declares public LiveCodeBench pass@1, maximize, on public v4/v5 proxy and confirmation splits, with the old greedy single-sample protocol. No fast-eval summary or completion receipt exists here, so the proxy score is not available. | | Final evaluator | Use only the manifest-selected full175 directories: livecodebench_v6_pass_at_1_full175, maximize, all 175 v6 problems, ten samples per problem, temperature 0.2, top_p 0.95, max_tokens 2048, avg@10. Each summary reports n=175 and n_samples=1750. | | Artifact contract | Each accepted checkpoint must be a complete loadable Hugging Face causal-LM. At most three valid artifacts are retained, and the official run result is the highest valid final score among them. |

The task source still contains first128 greedy fields; that is a package/protocol conflict and background only. Public proxy and hidden full175 evaluation differ in split, sample count, and generation protocol, so their numbers are not directly comparable.

3.2 How the baseline works

The baseline flow is: fixed model and fixed rows -> chat-template prompt/answer sequence -> labels retained only on assistant tokens -> completion NLL optimization -> updated full-model checkpoint export.

The fixed model and CodeForces rows are shuffled with the fixed split seed. A chat template creates a prompt-plus-answer sequence. The model produces next-token distributions, while labels set all prompt positions to -100 and retain only assistant-completion positions. A bfloat16 Hugging Face Trainer updates all model parameters with the existing optimizer and cosine-with-minimum-learning-rate schedule, using an effective batch of 18, and exports complete checkpoints at the configured steps. The agent explicitly identified the 60-step cap as the bottleneck; it did not establish another diagnosis with data or metric evidence.

4. Four-hour exploration and decision process

Only about 76 seconds were spent exploring: source inspection, one schedule patch, and a smoke/static-check attempt. No GPU training, proxy evaluation, parameter sweep, alternative objective, or data reweighting was run.

U-01 - Whether the formal horizon was the bottleneck

Motivation and hypothesis. The task requires a long-running formal recipe. The agent saw a normal 60-step endpoint and hypothesized that it would leave most of the twelve-hour budget unused.

Concrete change and experimental setup. It changed the run.sh and train.py defaults to 1,000,000 maximum steps and 250-step evaluation/save intervals, while keeping the completion-only objective, data split, seed, learning rate, and batch shape unchanged. No exploration training comparison was performed.

Observed result. There is no exploration loss or pass@1 result. Formal replay later stopped at step 852 through the injected wall-clock callback at roughly 49.29 seconds per step and produced three valid checkpoints.

Agent interpretation. The agent said the shipped 60-step recipe conflicted with the formal long-running requirement and that a wall-clock stop with periodic complete checkpoints should control completion.

Report assessment and confounds. Formal execution supports the engineering diagnosis, but it does not demonstrate that longer training improves code performance because there is no short-versus-long exploration control.

Decision and consequence. Adopted as the only candidate. It fixed formal budget utilization but supplied no scientific evidence of a quality gain.