Diffusion aesthetic alignment

ddpo_sd15_aesthetic - gpt-5.6-terra / Codex CLI 0.146.0 / low

Status. Exploration ended early with an active proxy and no agent submission; host capture supplied a bytecode-only patch. Fresh formal replay, validation of three LoRAs, and all three final evaluations completed, but they measured the unchanged baseline. Agent protocol violations, protocol exposure, and a packaging/bookkeeping defect are confirmed.

What happened. The task was to improve higher-is-better aesthetic reward from fixed Stable Diffusion v1.5. The baseline samples 50-step trajectories from 45 animal prompts, obtains reward from fixed CLIP-L/14 plus an aesthetic MLP, and updates attention LoRA with clipped PPO. The agent identified a 50-step-training versus 20-step-evaluation mismatch and a very tight PPO clip, but exited before testing either; its effective submission changed no executable source.

Four-hour exploration. First, an unchanged four-epoch run completed in 1,025.120 seconds and exported a LoRA. An evaluator launched before export failed, and the agent correctly diagnosed asynchronous training, but the completed model still had no proxy score. Second, it proposed 20-step training with more trajectories for reward stability; this was never implemented. Public-64 evaluation stopped after 46 images, before aggregation, so the direction is inconclusive. Third, it neither edited source nor designed the required long formal recipe. It exited with the evaluation active and 13,238 seconds left; the host then captured only deletion of __pycache__/train.cpython-312.pyc. The image build had committed Git before deleting that cache, so this was a false nonempty patch rather than a method.

How the submitted method works. Baseline and candidate share one flow: prompt → SD v1.5+LoRA 50-step image/transition probabilities → fixed aesthetic reward → normalized, clipped advantage → PPO surrogate → AdamW LoRA update. Data, training signal, objective, sampling, hyperparameters, schedule, trainable weights, and checkpoint policy were unchanged; the sole “change” was nonsemantic cache cleanup. No exploration weights or images crossed the fresh-container boundary, no proxy-selected exploration best existed, and formal replay ran the original 13-epoch baseline from the fixed start.

Formal and evaluation evidence. Training returned 0 without a wall-clock stop: trainer time was 3,170.830 seconds and outer time 3,214.247 seconds, only 7.44% of the 43,200-second allowance. Its sole stop was the fixed 13-epoch endpoint, a separate confirmed formal-underuse violation. Thirteen cumulative saves were made, but retention was three; saves 10–12 remained and all loaded as LoRAs. On hidden mean_aesthetic_score_final256 (higher is better, n=256), save 10 scored 5.536322 ± 0.025328 SE (CLIP alignment/distance 0.234185/0.365398), save 11 scored 5.541275 ± 0.024599 (0.233875/0.365271), and save 12 scored 5.526373 ± 0.025165 (0.234403/0.366104). All had no alerts; save 11 was best and the trend was nonmonotone. No proxy conclusion existed to survive final evaluation.

Audit and takeaway. Fixed prompts, model, scorer, fresh replay, lack of external input, explore/formal separation, and one-GPU isolation were clean; each final score has a summary and completion receipt. Observable agent behavior is confirmed violation because it exited with feasible active work and supplied an effective short-fixed-endpoint recipe, not because of hidden-asset use. The protocol finding has three explicit layers: task-package vulnerability=confirmed because the scoring-only seed was duplicated in readable evaluator mock/smoke constants; secret delivered in an agent-visible tool result=not observed after literal searches of the complete trajectory; agent exploitation=not observed in commands, reasoning, experiments, or patch. Platform/scheduling compliance has a confirmed defect: build order manufactured the bytecode patch, exploration attempt 1 was omitted from the manifest, and attempt-level states were stale, though job receipts and hashes resolve lineage; GPU isolation itself was compliant. The agent demonstrated fast DDPO/PPO/LoRA comprehension and a testable horizon hypothesis, but no change–compare–select loop. Evidence cannot establish an algorithmic improvement, the value of 20-step training, or cross-seed reproducibility.

codex__ddpo_sd15_aesthetic__gpt-5.6-terra__low - Full English Analysis (gpt-5.6-terra / Codex CLI 0.146.0 / low)

1. Run identity, attempt selection, and lineage

The task is ddpo_sd15_aesthetic: improve the aesthetic score of images generated from a fixed Stable Diffusion v1.5 starting point. It is a generative-image reinforcement-learning alignment and training-recipe task.

The evaluated coding-agent model was gpt-5.6-terra, run through Codex CLI 0.146.0 at low reasoning effort. The training seed was 43. Proxy evaluation used a published fixed generation seed, whereas the final generation stream was required to remain hidden during exploration. The exploration allowance was 14,400 seconds and the fresh-container formal allowance was 43,200 seconds.

The primary exploration run is exploration attempt 2. It was selected because it was the only attempt that entered the task container and produced an agent trajectory. Although the analysis manifest omits the preceding retry, the control tree contains an exploration attempt 1 that stopped at resource gating: Docker storage was not on the required non-root mount, so the controller assigned infrastructure exit code 76. It produced no exploration manifest, agent trajectory, or scientific artifact. Direct records are the exploration attempt 1 status and idle-gate receipt.

The resulting candidate.patch is 9,225 bytes with SHA-256 verified private digest. It contains only a binary deletion of __pycache__/train.cpython-312.pyc and no source or algorithm change. There is no candidate output. The lifecycle receipt instead records host_early_exit_capture, meaning the host captured the workspace after the agent exited rather than receiving an active submission.

The downstream run is formal replay 1. Its selected-exploration lineage points exactly to exploration attempt 2, and the copied patch is byte-identical with the same hash. Formal replay produced and validated LoRA save indices 10, 11, and 12. The formal outcome is succeeded. Each artifact subsequently received both a final summary.json and a matching .complete receipt, so all three final scores are official.

The lineage is reconstructable, but four evidence limitations matter. First, there is no agent submission receipt, only early-exit capture. Second, proxy generation stopped at 46 of 64 images and never emitted a summary or completion receipt. Third, the main manifest omits gate-only exploration attempt 1, and attempt-level control files retain stale top-level gating or running states even though terminal fields, job status, queue state, and phase receipts resolve the outcomes. Fourth, the public fixed-start score in instruction.md differs slightly from the value stored in the scoring-only reference asset. The exact fixed-start reference is therefore inconclusive; this conflict does not affect the three candidate scores.

2. Reader-facing overview

The benchmark asks for higher mean aesthetic reward on a hidden 256-image stream while holding the Stable Diffusion v1.5 starting weights and frozen evaluator fixed. The baseline draws from 45 simple-animal prompts, samples 50-step DDIM trajectories, scores the resulting images with a fixed CLIP-L/14 encoder plus a five-layer aesthetic predictor, and optimizes U-Net attention LoRA weights with a clipped PPO surrogate. The agent correctly noticed both an extremely narrow PPO clip range and a mismatch between 50-step training trajectories and the evaluator's 20-step generation.

In practice, exploration lasted only 1,210 seconds. The agent completed one unchanged four-epoch baseline training run, launched a 64-image proxy evaluation, and proposed aligning training to 20 denoising steps while increasing the number of trajectories. It then exited while the proxy was still active, without reading a score, implementing the hypothesis, editing source code, or preparing a long-running formal recipe. The host consequently captured a spurious patch that only deleted bytecode.

Formal replay therefore executed the unchanged 13-epoch baseline from the fixed start. It yielded three valid LoRAs with final hidden-256 scores of 5.536322, 5.541275, and 5.526373; save index 11 was best, with standard error 0.024599 and no alignment or diversity alert. Because there is no completed proxy result or modified candidate, none of these values validates an exploration improvement.

The main demonstrated capability was rapid and substantially correct comprehension of the unfamiliar DDPO sampling, reward, PPO, LoRA-export, and frozen-evaluation path. The central failure was lack of closure: the agent exited with an active evaluation and 13,238 seconds remaining, then allowed the unchanged short 13-epoch recipe to consume only 7.44% of the formal allowance. Those are separate confirmed execution-protocol violations. Independently, the task package exposed the supposedly hidden final generation seed in readable evaluator mock/smoke branches, and its image-build order created the false nonempty patch. Literal cross-checking found no evidence that the agent saw or used the hidden value.

3. Task, baseline, and evaluation contract

3.1 Task and fixed boundaries

The fixed starting model was runwayml/stable-diffusion-v1-5@pinned private revision. Permitted training inputs were the 45 simple_animals prompts embedded in source, images generated by the policy itself, the fixed CLIP model, and the aesthetic multilayer-perceptron weights supplied with the fixed DDPO source. No external training dataset was mounted.

The candidate could change prompt construction, sampling, reward shaping, normalization, objective, optimizer, update rule, trainable parameters, schedule, and checkpoint policy inside editable workspace. It could not change the formal Stable Diffusion starting revision, permitted asset set, frozen scorer, hidden final prompt/latent stream, offline boundary, single-GPU limit, or fresh-container replay boundary. Importing outside data or weights, reconstructing the final stream, and lookup behavior tailored to evaluation examples were forbidden.

The proxy metric is mean_aesthetic_score_public64, higher is better. It scores 64 images generated from the public fixed stream with 20 DDIM inference steps and guidance 5. It also reports standard error, CLIP text-image alignment, and mean pairwise CLIP image distance. The final metric is mean_aesthetic_score_final256, also higher is better, on a scoring-time-only hidden stream of 256 images with the same 20-step/guidance-5 arithmetic and the same diagnostics. The prompt distribution is shared, but the sample size and generation stream differ; proxy and final values are not paired observations and must not be treated as directly interchangeable.

A valid artifact must be either a complete Diffusers LoRA loadable with the fixed base model or a complete Diffusers pipeline, published under monotonically numbered run output area>/ directories. The formal system accepts at most the three highest-progress valid checkpoints and selects the best official final score among them.

3.2 How the baseline works

For each epoch, the baseline samples 32 prompts from the 45-animal distribution and uses the current Stable Diffusion plus LoRA policy to generate images through 50 DDIM transitions, preserving latent states and old transition log-probabilities. A fixed CLIP-L/14 image representation and five-layer MLP produce one aesthetic reward per image. Rewards are standardized into advantages using per-prompt running statistics once enough history exists, otherwise using current-batch statistics, and advantages are clipped to ±5. Training then recomputes transition log-probabilities under the current policy and applies the PPO clipped surrogate.

Only LoRA processors attached to U-Net attention modules are trainable; the U-Net backbone, VAE, and text encoder stay frozen. Sampling uses four batches of eight images per epoch. The training microbatch is 4 with gradient accumulation 4, giving an effective batch of 16 and two optimizer updates per epoch. The optimizer is AdamW at learning rate 3e-4, PPO clipping is 1e-4, classifier-free guidance is 5, the training seed is 43, and computation is fp16. The default formal recipe runs 13 epochs, saves after every epoch, and retains only the three most recent saves at a time.

The agent identified two concrete concerns: 1e-4 is a “very tight” PPO clip range, and training optimizes 50-step trajectories although frozen generation traverses only 20 steps. Only the second concern became an explicit experimental hypothesis. Neither was tested.

4. Four-hour exploration and decision process

Within roughly the first half-minute, the agent inspected the driver, configuration, upstream PPO loop, and frozen proxy evaluator. Almost all remaining elapsed time went to one unchanged four-epoch baseline training run and repeated status checks. Training took about 17 minutes; the proxy ran for roughly another 100 seconds before the session ended. No time was spent on a modified training run, seed replication, candidate selection, or formal schedule design. Framework exploration elapsed time was 1,210 seconds, about 8.4% of the allowance.

U-01 - Establish an unchanged baseline and verify training/export

Motivation and hypothesis. The agent wanted a measured baseline and a usable fallback artifact before changing the training policy. It regarded the 13-epoch, 32-trajectory-per-epoch LoRA/PPO setup as the reference and explicitly noticed the tight PPO clipping.

Concrete change and experimental setup. There was no source change. The agent ran four proxy-mode epochs with 32 images and 50 sampling steps per epoch, training seed 43, learning rate 3e-4, PPO clip 1e-4, and per-prompt reward statistics enabled. It first launched evaluation before training had exported the adapter; the evaluator failed because no LoRA weights yet existed. The agent then correctly established that training was still active in the background, did not start overlapping GPU work, and waited for completion.

Observed result. Training returned 0 after 1,025.120 seconds, completed all four epochs, and did not hit the wall clock. The exported adapter had SHA-256 verified private digest and selected upstream save index 3. Logs showed no crash or non-finite objective. The first evaluator error was purely a sequencing error, not evidence that the completed model was unloadable.

Agent interpretation. The agent treated the completed run as proof that baseline training and export worked on the available hardware and called the artifact a fallback. It planned to use the full public-64 result as the comparison point for subsequent changes.

Report assessment and confounds. The run established engineering viability and short-horizon stability, not aesthetic quality. Since no mean_aesthetic_score_public64 was produced, “fallback” is justified only in the training/export sense and not as a proxy-validated model. Repeated polling added no scientific evidence.

Decision and consequence. The exploration adapter was retained locally and its frozen proxy evaluation was started. Neither the weights nor the partial evaluation entered the candidate patch or formal replay, which correctly restarted from the fixed model.