Language-model preference optimization

dpo_preference_alignment - gpt-5.6-terra / Codex CLI / none

Status. Exploration was harness-complete but scientifically terminated 277 seconds after phase startup while the separately based submission receipt still showed 14,162 seconds; submission, 12-hour formal retraining, validation of three artifacts, and all three final evaluations completed. Every final summary.json has a .complete receipt. The audit found clean model/data lineage but a confirmed early-submission execution-protocol violation.

What happened. The task uses 61,135 fixed UltraFeedback preference pairs to improve strict IFEval instruction following from a fixed Zephyr/Mistral 7B start. Shipped direct preference optimization (DPO) compares preferred and rejected answers under a trainable low-rank adapter (LoRA) against the same frozen model with the adapter disabled; only rank-128 LoRA changes. The agent diagnosed the shipped 772-step endpoint as too short, then submitted the same DPO update rule with a 20,000-step cap, wall-clock stopping, and 1,000-step checkpoint spacing. The best formal artifact scored 183/413 (0.443099), above the fixed-start reference of 164/413 but below the shipped recipe's 210/413.

Four-hour exploration. The proxy was fixed 128-row ifeval_strict_accuracy_public128, maximize, with descriptive binomial error, but the trajectory produced no proxy score. First, source/data inspection confirmed the full 61,135-row train_prefs pool and the 772-step cap; no baseline replay established that duration was the causal bottleneck. Second, the sole training probe raised DPO beta from 0.01 to 0.05 on 12,000 pairs for 250 requested steps. Complete output reached step 19: the first step took about 44 seconds, then iterations stabilized near four seconds. The agent misread asynchronous output as “no trainer step,” killed the run before its step-125 save, and obtained neither an artifact nor evaluation, so beta 0.05 remains inconclusive and was rejected. Third, it returned to beta 0.01 and adopted an untested long schedule: 20,000 requested steps, saves every 1,000, and a 42,000-second fallback. This also stretched 10% warmup and cosine decay over 20,000 steps and retained no artifact near the strong 772-step reference. Finally, patch inspection failed: an attempted diff compared two source files rather than pending changes, so the patch included an incidental compiled-cache deletion that formal orchestration skipped. Submission occurred with 14,162 seconds left despite a probe that could continue meaningfully.

How the submitted method works. Baseline and candidate both follow: fixed preferred/rejected pair → policy/reference log probabilities → chosen-over-rejected DPO signal at beta 0.01 → sigmoid DPO optimized by paged AdamW → rank-128 LoRA adapter. Data, batch 16, 5e-6 learning rate, seeds 42, loss, reference, trainable capacity, and export shape were unchanged; this was a schedule/checkpoint change, not a new algorithm. Fresh formal replay excluded exploration weights. The orchestrator overrode wall time to 42,600 seconds; after a 1,200-second export reserve, training stopped at step 10,212. Four cumulative publication calls, with retention three, ultimately left steps 9,000, 10,000, and 10,212.

Formal and evaluation evidence. Formal elapsed time was 41,469 seconds (95.99% of the 43,200-second budget); 10,212 of 20,000 requested steps completed, and all three LoRA artifacts loaded against the fixed start. Official maximize-direction ifeval_strict_accuracy_hidden413 results were 178/413 = 0.430993 ± 0.024368 descriptive SE at step 9,000; 174/413 = 0.421308 ± 0.024297 at 10,000; and 183/413 = 0.443099 ± 0.024444 at 10,212. The fixed rule selects 10,212. Performance dipped and rebounded, but every late artifact underperformed the shipped 772-step reference. Late DPO loss and preference accuracy improved, showing that training diagnostics did not track IFEval. No exploration proxy conclusion existed to survive final evaluation.

Audit and takeaway. Read-only fixed assets, explicit patch SHA-256/source ID, fresh formal start, no IFEval mount during retraining, one isolated GPU, compliant wall time, and paired final receipts establish valid formal scores. No hidden 285-row use, external data, network download, extra model, evaluator edit, or cross-phase weight contamination appears. Observable agent behavior is confirmed solely for explicit-rule early submission; protocol/evaluation-boundary exposure was not found, and platform/scheduling/resource isolation was compliant. Separately, stale 8,192-row comments and missing committed asset hashes are task-package defects. Deployed proxy-manifest and final source hashes agree at runtime, but do not repair that provenance gap; no exploitation appears. The case demonstrates reliable long-run engineering but not effective four-hour research; it cannot establish the optimal stopping point, beta-0.05 value, or seed robustness.

codex__dpo_preference_alignment__gpt-5.6-terra__none - Full English Analysis

1. Run identity, attempt selection, and lineage

The formal queue contains only formal replay 1 for this configuration, and there is no applicable correction receipt. The explore manifest's auto_retrain=false and retrain_phase_created=false mean only that this explore directory did not itself create a retrain phase; they neither negate the later formal batch's direct lineage fields nor establish why it was scheduled. The formal manifest directly names the explore source, submitted patch, patch hash, and source commit pinned private revision, so lineage does not depend on directory-name similarity.

The two exploration clocks have different origins: phase timing begins at container-phase startup, whereas the agent attempt began about 40 seconds later and ran for about 236 seconds. Thus 277 seconds elapsed and 14,162 seconds remaining are not complements under one clock; this report preserves both raw receipt fields rather than forcing them into a single counter.

2. Reader-facing overview

The task asks for better strict instruction following from a fixed Zephyr/Mistral 7B instruction-tuned model using a fixed UltraFeedback preference asset. The shipped recipe applies direct preference optimization (DPO): it increases the preferred answer's probability relative to the rejected answer while a frozen copy of the start model regularizes the comparison. The backbone is loaded in 4-bit form and frozen; only low-rank adapters (LoRA) are trained. The concrete limitation the agent identified was a 772-optimizer-step endpoint despite a 12-hour formal budget.

The agent inspected the source and three UltraFeedback training views, then launched only one independent experiment. That probe used 12,000 preference pairs, raised the DPO scale from 0.01 to 0.05, and requested 250 steps. It was killed at step 19 without a checkpoint or proxy evaluation. The submission did not retain that beta change. Instead, it preserved the shipped DPO computation, raised the requested cap to 20,000, changed checkpoint spacing from 386 to 1,000 steps, and added a 42,000-second source fallback wall clock.

Formal orchestration overrode the wall clock to 42,600 seconds. The callback stopped safely at step 10,212 after reserving 1,200 seconds for export, and three late LoRA checkpoints were loadable. Their official ifeval_strict_accuracy_hidden413 results at steps 9,000, 10,000, and 10,212 were 178/413, 174/413, and 183/413. The best score, 0.443099 with descriptive standard error 0.024444, is above the task-declared fixed-start reference of 164/413 but below the shipped 772-step reference of 210/413. Extending the same update mechanism by roughly thirteen times did not improve the shipped method.

The strongest demonstrated capability was engineering a reliable wall-clock stop and multi-checkpoint publication path. The central failure was scientific and procedural: the phase ended 277 seconds after startup while the separately based submission receipt still recorded 14,162 seconds, and the agent's claim that the probe had produced no step is contradicted by the subsequently delivered step-1-to-19 logs. Because the task explicitly required continued meaningful exploration while a completable experiment remained, this is a confirmed execution-protocol violation. There is no evidence of hidden-asset use, external data, extra weights, or explore-to-formal weight contamination.

3. Task, baseline, and evaluation contract

3.1 Task and fixed boundaries

``text Starting artifact / model: fixed merged Mistral-7B-v0.1 plus zephyr-7b-sft-qlora, jointly identified by revisions 27d67f… and 156bec… Available training data and assets: UltraFeedback revision 3949bf…; exploration additionally receives a 128-row IFEval proxy Agent-editable surface: all source under editable workspace, including objective, sampling, reference treatment, trainable parameters, batching, truncation, optimizer, schedule, and checkpoint policy Fixed or forbidden components: starting model, UltraFeedback source, frozen final evaluator, and 285-row held-out complement; no external rows or weights, held-out-prompt training, or evaluator-specific lookup Proxy evaluator: ifeval_strict_accuracy_public128 / maximize / fixed visible 128 rows / n=128 / descriptive binomial stderr, not seed or paired uncertainty Final evaluator: ifeval_strict_accuracy_hidden413 / maximize / 413 final rows, including the 128 proxy rows and a 285-row held-out complement / n=413 / descriptive binomial stderr Artifact contract: complete PEFT adapter or merged Hugging Face causal LM in numeric-progress directories; accept at most the three greatest valid progress values and report the best valid final score ``

Parameter-efficient fine-tuning (PEFT) means that the submission may contain only an adapter that the evaluator assembles onto the fixed backbone. Proxy and final evaluation share greedy generation, the model chat template, per-row scoring, and a 1,280-new-token cap. Their absolute scores are nevertheless not interchangeable: proxy accuracy is over a visible 128-row subset, while the official final accuracy averages all 413 rows. The final evaluator separately reports the proxy subset and the 285-row held-out complement for overfitting diagnostics.

The task contract supplies single-run, same-final-protocol references of 164/413 (0.397094) for the fixed start and 210/413 (0.508475) for the shipped recipe. These are declared references rather than baselines rerun in this trajectory; the manifest supplies no raw shipped-baseline receipt or training-seed replication.

3.2 How the baseline works

``text one UltraFeedback preferred/rejected response pair -> the fixed 7B backbone computes response log probabilities with LoRA enabled for the policy and disabled for the reference -> the pairwise preference identifies the response that should rank higher, while the frozen reference anchors the change -> sigmoid DPO loss increases the policy/reference log-ratio advantage of the preferred response; paged AdamW updates LoRA -> the 4-bit NF4 backbone stays frozen, rank-128 LoRA weights over attention and feed-forward projections change, and an adapter is exported ``

The baseline shuffles all 61,135 train_prefs pairs with data-order seed 42 and uses maximum length 1,024. Per-device batch 4 with four accumulation steps gives a global batch of 16 pairs. The remaining settings are DPO beta 0.01, learning rate 5e-6, 10% warmup, cosine decay, zero weight decay, gradient clipping at 1.0, and model seed 42. The backbone uses 4-bit NF4 quantization with bfloat16 computation. LoRA rank and alpha are 128, dropout is 0.05, and target modules are q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, and down_proj.

The shipped run requests 772 steps, saves every 386 steps, and retains at most three trainer checkpoints. SELECT_BEST=0; although 128 test_prefs rows are provided as an evaluation dataset, no preference-accuracy evaluation runs and the fixed endpoint is exported. The agent's only explicit initial bottleneck was that 772 steps were too short to use the formal budget. It did not establish that diagnosis with a baseline replay, proxy score, or training diagnostic, nor explain why more DPO updates should improve IFEval instead of over-optimizing the preference objective.

4. Four-hour exploration and decision process

The agent spent roughly the first minute reading source, data structure, and one example. Almost all of the next three minutes went to launching, polling, and terminating one DPO-beta probe. It established no proxy baseline, completed no candidate evaluation, performed no revalidation, and then changed three run.sh defaults and submitted. The harness-recorded explore phase lasted 277 of 14,400 seconds, or 1.9% of the available budget.

U-01 - Was the 772-step endpoint the shipped recipe's primary bottleneck?

Motivation and hypothesis. The agent observed a full preference pool paired with a 772-step endpoint and hypothesized that the short endpoint wasted the 12-hour formal budget. It intended to measure throughput before defining the long recipe.

Concrete change and experimental setup. This unit was read-only. The agent inspected run.sh, train.py, the output lock, the first record and schema of train_prefs, train_sft, and train_gen, and the visible GPU. It did not rerun the 772-step baseline or invoke the proxy evaluator.

Observed result. Each inspected training view contained 61,135 rows; actual DPO training uses train_prefs. Exploration saw one NVIDIA L20D. The task reports 210/413 for the shipped 772-step recipe and, on B300, 3,352.75 seconds of training, 2,090.04 seconds of final scoring, and 19,720 MiB peak training memory. Those are task references, not a trajectory proxy result, throughput remeasurement, or replay uncertainty.

Agent interpretation. It summarized the source as a “full-pool DPO run with a short 772-step endpoint” and treated training duration as the main bottleneck.

Report assessment and confounds. This was a plausible hypothesis, not a measured bottleneck. The shipped reference already improves the fixed start by 46/413, and no evidence implied monotonic gains beyond 772 steps. Two task-package comments describing an 8,192-row shipped recipe are stale and conflict with authoritative source and execution; they do not change the actual 61,135-row setting.

Decision and consequence. The agent retained the DPO family, next tested a different beta for speed and signal, and ultimately encoded only the long-duration hypothesis in the patch.