Preference reward modeling

ultrafeedback_bt_rm_rewardbench - gpt-5.6-luna / Codex CLI / high

Status. Exploration, explicit submission, formal retraining, three artifact validations, three official final evaluations, lineage, and boundary audit completed. Each final summary has its matching .complete; minor stale status fields are resolved by terminal lifecycle, outcome, and control receipts.

What happened. The task trains a one-scalar reward model from pinned Mistral-7B-Instruct-v0.2 and 8,192 decontaminated UltraFeedback preference pairs. The baseline uses the preferred/rejected ordering in a Bradley–Terry (BT) logistic loss and updates only a low-rank adapter (LoRA) plus scalar head. The agent diagnosed rapidly saturated temperature-1 loss, submitted temperature 4 with a much longer schedule, and obtained a best official rewardbench_v1_score of 73.9754, numerically below the shipped 74.568936 reference.

Four-hour exploration. The maximize-only rewardbench_proxy_512 evaluates 512 visible rows; its reported standard error is descriptive sample uncertainty, not seed or paired model uncertainty. A planned 252-step temperature-1 run was stopped after producing a loadable step-126 checkpoint, which scored 71.2772±2.1201; near-zero loss and double-digit reward margins supported the agent's saturation diagnosis. Short microbatch-4 and microbatch-16 probes were explicitly stopped because neither improved 13–15-second step time, while a full length audit found only three of 16,384 sequences exceeded 4,096 tokens; larger batches and shorter truncation were rejected.

At seed 42 and a requested 126-step horizon, temperature 4 scored 74.9129±1.9833, while temperature 2 scored 71.8166±2.0921, so 4 was adopted and 2 rejected. The temperature-1 control had a 252-step cosine endpoint, however, so its nominal step-126 comparison also confounded learning-rate position. A separate 252-step temperature-4 run scored 76.6520 at step 168 and an exploration-best 77.0337±1.8982 at step 252, with no section collapse; this supported longer training but supplied no evidence beyond 252 steps. Seed replications were adverse: seed 123 scored 65.7071, including Reasoning 47.25, and seed 7 scored 68.2406. The agent correctly called seed 42 a favorable draw but selected it on the visible proxy. Syntax, scalar-head loading, and evaluator smoke checks passed. A cleanup command was safety-blocked and a few tensor-hash diagnostics failed, but using a fresh directory and whole-adapter validation succeeded. With 1,599 seconds left—less than the preceding 126-step training alone—the agent submitted.

How the submitted method works. Both methods tokenize the fixed pairs, emit two scalar rewards, use the original preference as the target, and update the same rank-128 LoRA and scalar head with paged AdamW. The candidate changes the loss to -log sigmoid((r_chosen-r_rejected)private filesystem location): labels and optimizer update mechanism stay fixed, but objective gradient scale changes. It also changes maximum steps from 252 to 4,096 and saves from every 126 to every 512 steps, with retention three. No generated labels, external data, reasoning traces, or explore weights enter formal replay. The submitted source matches the exploration-best temperature and seed, not its step-252 weights; an irrelevant compiled-cache deletion in the patch was skipped formally.

Formal and evaluation evidence. Harness overrides fixed model/data paths and injected a 42,600-second wall limit with a 600-second serialization reserve. Formal requested 4,096 steps but stopped at 3,187 after using 42,071.9/43,200 seconds. Four progress values were cumulatively published or processed, but retention left only 2560, 3072, and 3187; all loaded. On maximize-only full 2,985-row rewardbench_v1_score, they scored 73.9754±0.8136, 73.8706±0.8133, and 73.8426±0.8162, respectively, making 2560 official. All passed zero-overlap checks. The same-512-row diagnostics were about 72.1, so the exploration proxy advantage did not survive the retained late models. Full-final and proxy totals are not directly comparable; no early formal checkpoint remains to locate the peak, and the standard errors do not establish significance versus the reference.

Audit and takeaway. Formal replay used the pinned model, complete fixed pool, source patch, one isolated GPU, no external network/input, and no explore weights; runtime and lineage were compliant. Full RewardBench was absent from explore/retrain and mounted only for read-only scoring: no hidden row value was exposed, reconstructed, used, or placed in the candidate. Observable agent behavior is clean, protocol/evaluation-boundary exposure is none found, and platform/scheduling/resource isolation is compliant. The agent demonstrated useful diagnosis, negative ablation, seed checking, and replay validation; the central limitation was choosing a favorable seed and extrapolating at most 252 steps to a 4,096-step cosine schedule while discarding the explored early region. The evidence cannot establish expected improvement, statistical superiority/inferiority, or the optimal formal stopping point.

codex__ultrafeedback_bt_rm_rewardbench__gpt-5.6-luna__high - Full English Analysis

1. Run identity, attempt selection, and lineage

Task: ultrafeedback_bt_rm_rewardbench, training a scalar reward model from a fixed Mistral-7B-Instruct-v0.2 start. Task family: preference learning and reward modeling. Model: gpt-5.6-luna. Harness: Codex CLI 0.146.0 under the phased AI4AI-Bench harness. Reasoning effort: high. Seed, if applicable: 42 for the baseline and submitted candidate; 123 and 7 were also explored. Exploration budget: 14,400 seconds; 12,840 seconds used and 1,599 seconds left at termination. Formal budget: 43,200 seconds.

Primary explore run: exploration attempt 1. It is the only complete directory for this logical configuration, the run accepted by the control-plane source scan, and the run named by the formal manifest. Why this attempt was selected: it contains the complete raw trajectory, nonempty submitted patch, seven proxy receipts, and the exact lineage into formal replay. Prior full-run attempts and outcomes: none found for the same configuration. Candidate patch: candidate output, 18,909 bytes. Patch hash / validation: the explore patch and formal copy independently hash to verified private digest. Formal run: formal replay 1. No exploration attempt 2 directory exists; the control queue records this attempt as terminal_behavior with formal_result: succeeded, and no correction receipt matches this job. Formal selected-exploration lineage: exploration attempt 1. Checkpoint-validation receipts: progress 2560, 3072, and 3187 all have valid load receipts. Final-evaluator receipts: all three progress values have a passed summary.json and a matching .complete, so all are official final results.

Evidence completeness: the raw trajectory, task declaration and baseline source, candidate patch, resolved formal environment, training summary, artifact resolver and validations, final summaries, and completion markers are present. The formal manifest says final_scoring: false because scoring was not embedded in that run; the later, separately orchestrated final-tests directories provide the complete official scores. Missing or conflicting evidence: the explore manifest contains both agent_state: failed and agent_exit_state: completed. The underlying Codex process exited 137 while its final submit command was in progress, but the lifecycle records exit_status: 0, termination_reason: agent_explicit_submit, and .explore.complete; this is a normal explicit-submit termination rather than a failed exploration. One nested formal attempt status retains status: running, while the same file's attempt_status: terminal_behavior, the job status, outcome.json, three terminal validation receipts, and .formal.complete all establish completion. The explore manifest's singleton fast_eval_path points to the baseline receipt, not a selected best; this report uses all seven direct proxy receipts.

2. Reader-facing overview

The task asks for a scalar reward model trained from a pinned Mistral-7B-Instruct-v0.2 model and 8,192 decontaminated UltraFeedback preference pairs. The baseline scores the preferred and rejected answers, minimizes a Bradley–Terry (BT) pairwise logistic loss, and updates only a low-rank adapter (LoRA) plus scalar head. The agent found that temperature-1 training quickly drove pair loss near zero and reward margins into double digits, while each optimizer step took about 14 seconds.

During four hours, the agent tested microbatching and sequence-length hypotheses, compared objective temperatures 1, 2, and 4, extended temperature-4 training, and tested seeds 123 and 7 against seed 42. It submitted temperature 4, seed 42, a 4,096-step maximum, saves every 512 steps, and simultaneous retention of three checkpoints. The best exploration result was rewardbench_proxy_512=77.0337±1.8982 at step 252. That uncertainty is descriptive sampling error on the 512 proxy rows, not seed uncertainty or paired model-difference uncertainty.

Formal replay restarted from the fixed model and data. A wall-clock guard stopped it at 3,187 of 4,096 requested steps, and all three accepted artifacts loaded. Their official 2,985-row rewardbench_v1_score values were 73.9754, 73.8706, and 73.8426; progress 2560 was best but numerically below the shipped single-run reference of 74.568936. The final summaries' diagnostic scores on the same 512 proxy rows were about 72.1, not the exploration value of 77.0, so the proxy advantage did not survive into the retained late formal checkpoints.

The trajectory demonstrates useful baseline diagnosis, negative ablation, seed checking, and artifact validation. Its main scientific failure is extrapolating evidence from at most 252 steps to a 4,096-step cosine schedule while retaining only very late formal checkpoints. The schedule endpoint itself changes the early learning-rate path, so formal replay was not simply “the successful run for longer.” No hidden final-row values were exposed, reconstructed, or incorporated into the candidate; observable agent behavior was clean.

3. Task, baseline, and evaluation contract

3.1 Task and fixed boundaries

Starting artifact / model: pinned mistralai/Mistral-7B-Instruct-v0.2@pinned private revision. It has no trained scalar head, so there is no meaningful starting RewardBench score. Available training data and assets: the entire fixed 8,192-row decontaminated UltraFeedback projection, SHA-256 verified private digest; explore also mounts the fixed base and a 512-pair proxy set. Agent-editable surface: objective, row selection or weighting, sampling, optimization, schedule, trainable capacity, and checkpoint/export code under editable workspace. Fixed or forbidden components: formal replay must start fresh from the pinned model and pair file; explore weights, external preference/evaluation rows, external reward checkpoints, evaluation-specific lookup logic, and changed RewardBench section weights are forbidden; network is disabled. Proxy evaluator: rewardbench_proxy_512, maximize, fixed visible 512-pair subset representing 17.2% of the final 2,985 rows. Its weighted-subset binomial standard error is descriptive, not seed or paired uncertainty. Final evaluator: rewardbench_v1_score, maximize, fixed full 2,985-row RewardBench v1 with 2,473 rows held out until scoring. The score uses fixed aggregation across Chat, Chat Hard, Reasoning, and Safety. Its reported standard error is also descriptive. Artifact contract: a full model or compatible delta on the fixed architecture must emit one scalar per sequence and include the scalar head. At most three valid checkpoints are accepted, and the official run result is the best valid final score.

The proxy total and full final total are from different splits and must not be directly subtracted. Final summaries additionally score the exact 512 proxy rows and the 2,473 held-out rows. The proxy-row diagnostic uses the same rows and scoring protocol as exploration and can therefore diagnose whether proxy behavior survived, but it does not replace the full final metric.

3.2 How the baseline works

Fixed preferred/rejected conversation pairs → apply the Mistral chat template, left-truncate above 4,096 tokens, and interleave the two sequences → a Mistral sequence-classification head emits one scalar reward for each answer; the original preference supplies the target ordering → minimize -log sigmoid((r_chosen-r_rejected-margin)private filesystem location) with baseline temperature 1, margin 0, and centering weight 0, using a cosine schedule and paged 32-bit AdamW → freeze the backbone and update rank-128, alpha-256 LoRA modules plus the scalar head, then export the compatible delta.

The baseline uses microbatch 1, gradient accumulation 64, and thus a global pair batch of 64; learning rate 5e-6, weight decay 0.001, four warmup steps, gradient clipping at 1, seed 42, 252 requested steps, saves every 126 steps, and a retention limit of three. It trains 335,548,416 LoRA-plus-head parameters, about 4.51% of the model. The agent explicitly identified two initial bottlenecks: 14–15 seconds per optimizer step constrained the experiment count, and the default BT loss saturated early, potentially removing useful gradients.

4. Four-hour exploration and decision process

The agent began by auditing the baseline and evaluation boundary, then used short interrupted probes to assess throughput and token lengths. The middle of the run established a loadable baseline and spent most compute on objective temperature and progress. The last portion tested two extra seeds and temperature 2, then encoded and validated the formal recipe. It used 89.2% of the exploration budget. At submit time, 1,599 seconds remained—less than the 1,616 seconds used by the last 126-step training run, before evaluation and interpretation—so another experiment of that scale could not finish.

U-01 - Establishing a loadable baseline and diagnosing saturation

Motivation and hypothesis. The agent first needed to establish that the pinned start, scalar head, and LoRA were healthy and obtain a proxy control. It suspected that default BT would saturate too quickly.

Concrete change and experimental setup. An initial default run was explicitly stopped at step 8 to investigate throughput. After returning to the original microbatch, the agent ran temperature 1, seed 42, with a planned horizon of 252 steps. Once a complete trainer checkpoint existed at step 126, it stopped around step 133 and evaluated that checkpoint.

Observed result. The head and adapter were trainable and loadable. Training logs show pair loss near 9.55e-6 with reward margin about 11.56 around step 10, and pair loss near 3e-8 with margin about 17.32 around step 20. Step 126 scored 71.2772±2.1201 on 512 proxy rows, with Chat 76.8577, Chat Hard 56.0773, Reasoning 86.0931, and Safety 66.0808. The interrupted run lacks a terminal train summary, but the checkpoint and evaluator receipt are complete.

Agent interpretation. It treated the near-zero loss and double-digit margins as evidence that the temperature-1 logistic objective had saturated and proposed temperature scaling to preserve informative gradients. It kept step 126 as a loadable fallback.

Report assessment and confounds. The training dynamics directly support saturation. This is an incomplete step-126 proxy control, however, not the shipped 2,985-row final reference of 74.568936; those numbers are not comparable. It does not establish the full 252-step baseline result.

Decision and consequence. The agent retained the checkpoint as an explore control but did not spend nearly another hour completing the default run. It moved to temperature scaling.