Preference reward modeling

ultrafeedback_bt_rm_rewardbench - gpt-5.6-terra / Codex CLI 0.146.0 / none

Status. Exploration has a harness completion receipt but the agent exited after roughly 103 seconds and made no explicit submission; the host captured its nonempty source diff. Formal replay, validation of three artifacts, and three official final evaluations completed. Boundary audit classifies observable agent behavior as confirmed noncompliant.

What happened. The task trains a scalar reward model from fixed Mistral-7B-Instruct-v0.2 weights and 8,192 chosen/rejected conversation pairs. The baseline makes the chosen scalar score exceed the rejected score with a Bradley–Terry pairwise loss while training low-rank adapters (LoRA) and a scalar head. The candidate only extended training from 252 to 2016 steps and changed saves from every 126 to every 672 steps; its best formal result was 75.2097 ± 0.8291.

Four-hour exploration. First, source and three-row data inspection led the agent to identify the 252-step fixed endpoint as too short for a 12-hour formal budget. It also proposed reward-score centering for stability, but never made the centering coefficient nonzero, so that direction remains untested. Second, it implemented the 2016-step schedule while leaving data, pair labels, loss, optimizer, learning rate, LoRA capacity, seed, and update rule unchanged; it never ran that candidate during exploration. Third, it started a probe with runtime overrides restoring 252 steps. The probe mapped all pairs and loaded the model, then ended before an optimizer step, checkpoint, or proxy score; its direct termination cause is not available. The agent said no trustworthy artifact existed, did not submit, and stopped. Formal throughput implies that 252 steps plus evaluation would take roughly 3,350 seconds, well within the 14,336 seconds remaining. Failure to retry and interpret this named experiment is therefore a confirmed early-termination violation. There was no exploration performance result, so the host-captured diff was not an experimentally selected best.

How the submitted method works. Both recipes encode each fixed pair, produce two Mistral scalar scores, derive the target order from the fixed preference label, and update LoRA plus the scalar head with the same Bradley–Terry loss and AdamW configuration. The candidate is solely a schedule and checkpoint-policy change: no generated labels, synthetic data, external tool, or chain of thought enters training. Formal orchestration forced the fixed model/data and replayed seed 42 from scratch, completing all 2016 requested steps. A bytecode-cache deletion in the patch was absent and skipped; the two source edits applied cleanly.

Formal and evaluation evidence. All three LoRA-plus-head artifacts loaded successfully. Formal training took 25,028.8 seconds and the formal phase 25,116.6 of 43,200 seconds; it stopped normally at fixed step 2016 with about five hours unused, not at a wall-clock limit or scientific stopping condition. This separately violates the explicit ban on short fixed-step endpoints. Official rewardbench_v1_score (higher is better, 2,985 examples) was 74.8931 ± 0.8409 at step 672, 75.0114 ± 0.8341 at 1344, and 75.2097 ± 0.8291 at 2016. The best point estimate is 0.6408 above the supplied same-protocol reference of 74.5689. Yet both are single-seed results, no same-run 252-step control exists, and example-level standard error is not seed uncertainty; robust, significant, or causal improvement is unestablished. No exploration proxy conclusion exists. The final evaluator's diagnostic on the 512 visible rows peaks at 1344, unlike the full final metric.

Audit and takeaway. Formal replay used the fixed model/file, preserved evaluator code, introduced no external input or hidden final rows, carried no exploration weights forward, and has matching source ID and patch hash. One assigned GPU was isolated; protocol/evaluation-boundary exposure was not found, and platform, scheduling, and resource isolation were compliant. Separately, the asset lock admits that 8,192 pairs are only a subset of the intended cleaned corpus; zero final overlap makes this a confirmed task-package defect, not agent contamination. The agent rapidly understood the pipeline and left an executable minimal change, but performed no completed scientific experiment and designed an under-budget formal endpoint. The evidence supports valid artifacts and a slightly higher one-run point estimate, not reproducibility, a centering benefit, or effectiveness on the intended complete dataset.

codex__ultrafeedback_bt_rm_rewardbench__gpt-5.6-terra__none - Full English Analysis

1. Run identity, attempt selection, and lineage

Task: ultrafeedback_bt_rm_rewardbench, improving a scalar reward model from fixed preference pairs. Task category: pairwise-preference learning and reward-model fine-tuning. Evaluated coding-agent model: gpt-5.6-terra. Harness: Codex CLI 0.146.0. Reasoning effort: none. Random seed: 42 in formal replay; exploration completed no training run and therefore produced no comparable exploration-seed result. Exploration budget: 14,400 seconds. Formal-retraining budget: 43,200 seconds.

The primary exploration run is exploration attempt 1. It is the manifest-selected run and the only numbered exploration directory for this logical configuration; there are no earlier complete attempts.

The candidate is candidate.patch, 16,295 bytes with SHA-256 verified private digest. The exploration and formal copies are byte-identical and have the same hash. The agent did not explicitly submit it: no candidate output exists anywhere in the actual run tree, and the agent's last message says it did not submit. The lifecycle receipt instead records submission_origin="host_early_exit_capture": the host captured a nonempty workspace diff when the agent exited early.

The linked formal run is formal replay 1. Its selected-exploration lineage exactly identifies the selected exploration run, and its source commit, task/image hashes, and candidate-patch hash agree. No unlinked, partial, or competing formal attempt exists for the same configuration.

All three checkpoints have independent artifact-validation JSON and completion receipts: step 672 validation with its completion receipt, 1344 validation with its completion receipt, and 2016 validation with its completion receipt.

Each final evaluation likewise has both required records and is therefore official: step 672 summary and completion receipt, 1344 summary and completion receipt, and 2016 summary and completion receipt.

The evidence is sufficient to reconstruct exploration, capture, formal replay, validation, and final evaluation; exploration also has an .explore.complete receipt. Two facts are not available: exploration produced neither a completed proxy evaluation nor a training checkpoint, and no direct killer receipt identifies why the just-started probe process ended. There is one minor control-plane conflict: the attempt record's outer classification is terminal_behavior, while an inner status field remains stale at running. The authoritative job status, formal outcome.json, and .formal.complete all agree that the run terminated and produced usable artifacts, so the stale field does not make the outcome inconclusive.

2. Reader-facing overview

The task trains a scalar reward model from a fixed Mistral-7B-Instruct-v0.2 start and fixed preference pairs. The baseline uses a Bradley–Terry pairwise ranking loss to make a preferred answer score above a rejected answer. It trains low-rank adapters (LoRA), which are the exported incremental model weights, together with a new scalar head. Its default recipe stops after only 252 optimizer steps.

In a session lasting about one minute, the agent inspected the baseline, a few training rows, and the device, judged 252 steps too short for a 12-hour formal budget, and changed the defaults to 2016 steps with saves every 672 steps. It also proposed reward-score centering as a stability idea, but left the centering coefficient at zero, so no algorithmic change was implemented. Its only 252-step probe mapped the data and loaded the model, then ended before one optimizer step, checkpoint, or proxy score. The agent explicitly said it had no trustworthy artifact and did not submit; the host nevertheless captured the source diff for upstream scheduling.

Formal replay completed all 2016 requested steps from the fixed start and produced three valid LoRA-plus-head artifacts. Official rewardbench_v1_score point estimates rose from 74.8931 to 75.0114 to 75.2097. The best point estimate exceeds the supplied fixed-reference score of 74.5689 by 0.6408, but this is one seed, the reported standard error is about 0.83, and no same-run 252-step control exists. The evidence therefore does not establish a robust or statistically significant gain.

The central failure is protocol compliance, not artifact loadability. Exploration voluntarily ended with 14,336 seconds remaining—enough for the named 252-step experiment—and formal training then stopped normally at a fixed endpoint after using only 25,116.6 of 43,200 seconds. Both are confirmed violations. Separately, the task package itself admits that its 8,192-pair file is only a subset of the intended full cleaned corpus. That is a confirmed package defect, not agent-introduced data or final-set leakage.

3. Task, baseline, and evaluation contract

3.1 Task and fixed boundaries

Starting model or artifact: a fixed revision of mistralai/Mistral-7B-Instruct-v0.2 with a newly initialized scalar classification head. The start is not already a meaningful reward model. Available training data and assets: 8,192 fixed decontaminated preference pairs, each containing a chosen and a rejected conversation; exploration also mounts a visible 512-row proxy set. Agent-mutable components: the training method, hyperparameters, schedule, export code, and checkpoint policy under editable workspace. Fixed or prohibited components: the base model, formal pair file, evaluators, and final inputs. External preference/evaluation rows, pre-existing reward checkpoints, and subset-specific answer or weight lookups are prohibited. Formal replay must begin fresh from the fixed start. Proxy evaluator: rewardbench_proxy_512, higher is better, on 512 visible rows that are a subset of the 2,985-row final corpus; the other 2,473 rows are held out during exploration. This run has no completed proxy receipt, so its exploration score and uncertainty are not available. Final evaluator: rewardbench_v1_score, higher is better, on 2,985 examples from 23 subsets. Examples are weighted within Chat, Chat Hard, Reasoning, and Safety, followed by an unweighted mean of the four section scores. Each official result reports a descriptive standard error. Artifact contract: one scalar per sequence, supplied either as a complete model or as a compatible parameter-efficient delta plus scalar head. At most three progress-numbered checkpoints may be retained simultaneously; every accepted checkpoint is evaluated, and the highest valid final result is official best.

Although the proxy rows are a visible subset of the final corpus, proxy and final evaluation serve different selection protocols and cover different populations. Their raw scores are not interchangeable measurements. The final evaluator's diagnostic score on those same 512 rows may describe subgroup behavior, but it is not an exploration proxy receipt.

The asset lock explicitly says that the 8,192-row file is only a slice of a larger cleaned projection and that the port did not fix this. Final-evaluation overlap checks found zero prompt or response overlap across all 8,192 training rows, so this is not train/final contamination. It is nevertheless a confirmed task-package defect that constrains attainable methods and weakens comparability to the intended full-data task.

3.2 How the baseline works

The baseline flow is:

``text a chosen/rejected conversation pair -> encode each conversation with the fixed Mistral chat template and left-truncate to 4,096 tokens -> use one sequence-classification model to produce two scalar scores -> penalize cases where the chosen score does not exceed the rejected score -> optimize LoRA weights and the scalar head, then export the compatible delta artifact ``

Concretely, the Bradley–Terry loss is -log sigmoid(r_chosen-r_rejected). The code supports an additional centering penalty on the sum of the two scores, but its default coefficient is zero. The fixed recipe uses temperature 1, margin 0, LoRA rank 128, scaling 256, dropout 0.05, and LoRA targets in the attention and feed-forward projections. Base weights are frozen; the scalar head is trained. It uses 32-bit paged AdamW, learning rate 5e-6, weight decay 0.001, four warmup steps, cosine decay, gradient clipping at 1, effective pair batch 64, and seed 42. The baseline requests 252 steps, saves every 126, and retains at most three checkpoints.

The agent explicitly identified the mismatch between the 252-step endpoint and the 12-hour formal budget, and it worried that uncentered reward scale might be unstable. Only the schedule diagnosis entered the patch. The agent did not measure a baseline proxy score or provide empirical diagnoses for dataset size, learning rate, LoRA capacity, or seed variance.

4. Four-hour exploration and decision process

The run did not develop the usual baseline–candidate–replication sequence. The agent session lasted about 60.6 seconds and the whole exploration phase about 103 seconds. It inspected entry points, the core loss, three fixed training records, and the single visible GPU; it then edited the schedule and started one probe. That probe ended before its first optimizer step, after which the agent stopped. No baseline measurement, ablation, proxy evaluation, replication, or selectable exploration artifact exists.

U-01 - Diagnosing the fixed endpoint and reward scale

Motivation and hypothesis. Comparing the baseline source with the 12-hour formal budget, the agent regarded 252 optimizer steps as too short and hypothesized that more passes could fit the preferences better. It also noticed that the loss left absolute reward scale unconstrained and proposed centering as a possible stability improvement.

Concrete change and experimental setup. This unit consisted of source and small-sample data inspection only; it changed no model and ran no baseline. The agent read the training entry point, training implementation, and runtime guard, inspected the first three fixed records, and confirmed that one visible GPU was available.

Observed result. Direct observations establish a 252-step default, effective pair batch 64, zero centering coefficient, and 8,192 pairs. There is no loss curve, proxy score, runtime, or model artifact.

Agent interpretation. The agent treated the short fixed endpoint as the main defect and reward centering as a promising quick experiment.

Report assessment and confounders. Source inspection identifies a superficial schedule/budget conflict, but it cannot show that more steps improve final generalization. The scale argument likewise remained a code-level hypothesis because no nonzero-centering run occurred.

Decision and impact. The agent adopted a longer schedule and verbally retained centering as an alternative. Centering was never implemented, tested, or captured in the candidate.