Diffusion aesthetic alignment

ddpo_sd15_aesthetic - gpt-5.6-luna / Codex CLI / high

Status. Exploration, explicit submission, formal retraining, artifact validation, hidden final evaluation, and audit all completed. Formal attempts 001 and 002 were authoritatively invalidated after unrelated processes joined their physical accelerator; fresh exploration attempt 3 is the only scientific source, and all three of its final artifacts have both summary and completion receipts.

What happened. The task is to improve aesthetic alignment from a fixed Stable Diffusion v1.5 start. The shipped Denoising Diffusion Policy Optimization (DDPO) recipe generates images on policy from 45 animal prompts, assigns rewards with a fixed CLIP-L/14 aesthetic network, normalizes and clips advantages, and uses a PPO-like clipped objective to update only rank-four Low-Rank Adaptation (LoRA) weights. The agent submitted no new algorithm: it extended the outer schedule from 13 to 40 epochs. The best hidden-final score was 5.617847 ± 0.024829, 0.091474 above the shipped solution under the same protocol.

Four-hour exploration. The public proxy maximizes mean_aesthetic_score_public64 over 64 images; its standard errors do not include training-seed variation, and all training used seed 43. The unmodified recipe rose from 5.498931 ± 0.042570 at four epochs to 5.542108 ± 0.051288 at eight and 5.594094 ± 0.050411 at thirteen, so the agent adopted longer training. Its contemporaneous comparison of the last proxy score to a shipped final score was cross-protocol and invalid. A joint change to 20 denoising steps, global reward normalization, and clip 0.01 scored 5.439228 ± 0.049151 at four epochs and was rejected, though the three causes remain entangled. Two inner passes plus clip 0.01 scored 5.447104 ± 0.053372 and was rejected. A larger sampling batch preserved 32 trajectories per epoch and cut four-epoch runtime from 1,303 to 847 seconds, but scored 5.477177 ± 0.058874; it was faster and more diverse but rejected on the primary metric. Temporary knobs for LoRA rank, DDIM noise, and timestep selection were never evaluated and were reverted. A 13-epoch export passed frozen load validation. The agent submitted after 10,914 of 14,400 seconds without a second seed, a research-completeness limitation.

How the submitted method works. Both recipes follow prompt → 50-step image trajectory → fixed aesthetic reward → reward normalization and clipped advantage → clipped policy objective → LoRA update. Reward, prompts, sampling batch, learning rate, trainable parameters, and update rule are unchanged. Raising the schedule from 13 to 40 epochs increases planned trajectories from 416 to 1,280 and gradient updates from 26 to 80; the patch also removes irrelevant tracked bytecode. Exploration weights were not submitted or reused. formal replay 3 applied only the source patch to the fixed start and actually completed epochs 0–39.

Formal and evaluation evidence. exploration attempt 3 stopped normally at the fixed schedule, not a wall-clock limit. Retraining took 9,833 seconds, 22.762% of the 43,200-second allowance. Forty cumulative saves were attempted, only the latest three were simultaneously retained and published, and progress 37, 38, and 39 all validated as loadable LoRA artifacts. No formal public64 scores are available. On mean_aesthetic_score_final256, maximized on the hidden split with n=256, progress 37 scored 5.617797 ± 0.024913, progress 38 scored 5.617847 ± 0.024829, and progress 39 scored 5.615683 ± 0.025302. All had no diagnostic alerts. The tiny tail differences indicate a plateau. The longer-training conclusion survives only through the same-final-protocol comparison with the shipped 5.526373 reference, not through a proxy-to-final subtraction.

Audit and takeaway. Fixed assets, the read-only evaluator, absent external input, hidden-instance mounting, one-device use, fresh explore-to-formal lineage, runtime, and receipts were checked. No hidden prompt or latent value reached the exploration record, no reconstruction or use was observed, and none affected the candidate: observable agent behavior is clean, protocol exposure was not found, while platform/resource isolation has a confirmed defect limited to the two discarded formal runs. The case demonstrates useful controlled screening and a minimal schedule improvement, but one seed, mixed-factor ablations, an untested 40-epoch extrapolation, and 22.8% formal-budget use cannot establish optimality, cross-seed robustness, or a novel algorithm.

codex__ddpo_sd15_aesthetic__gpt-5.6-luna__high - Full English Analysis

1. Run identity, attempt selection, and lineage

• Task: ddpo_sd15_aesthetic.
• Task category: reinforcement-learning post-training and aesthetic alignment for a diffusion generative model.
• Evaluated model, harness, and reasoning effort: gpt-5.6-luna; Codex CLI; high.
• Random seed: exploration training and the valid formal replay both used 43; final evaluation used an independent hidden random stream.
• Budgets: exploration allowed 14,400 seconds and used 10,914; formal replay allowed 43,200 seconds and valid exploration attempt 3 used 9,833.284.
• Primary exploration run and selection: exploration attempt 1 is the only complete exploration run referenced by the manifest, with no earlier exploration attempt available. Its raw trajectory contains 21 agent messages and 227 completed commands, ran from 2026-08-10 20:35:37 UTC to 23:37:31 UTC, and explicitly submitted with 3,526 seconds left.
• Earlier complete exploration reruns: not available, because no earlier complete exploration rerun exists.
• Candidate patch and validation: exploration attempt 1 produced SHA-256 verified private digest; formal replay 3 ingested the identical patch.
• Formal run and source: attempts 001 and 002 finished training, but authoritative corrections established unrelated external processes on the same physical accelerator. Both were made forensic-only and scientifically invalid, with reuse prohibited. exploration attempt 3 trained afresh and is the only valid formal run; its the formal replay is cryptographically linked to the selected exploration.
• Checkpoint-validation receipts: exploration attempt 3 progress 37, 38, and 39 were all accepted by the frozen validator as valid LoRA artifacts.
• Final-evaluator receipts: only exploration attempt 3 artifacts 37, 38, and 39 are used. Every artifact has both .complete and out/summary.json, so all three are official. exploration attempt 2 has no final-evaluation root.
• Result classification: successful formal replay with three valid official artifacts and a best score above the same-protocol shipped solution, but scientific evidence from only one training seed.

The lineage is “exploration exploration attempt 1 → candidate patch → fresh formal replay 3 → independent LoRA artifacts at progress 37/38/39 → a 256-image hidden final evaluation for each artifact.” No exploration checkpoint, cache, or weight was carried into formal replay 3, and neither invalid formal run contributed an artifact. One exploration attempt 3 status document retains a stale internal status=running field, but the same record has an end time and terminal_behavior, while the job status, outcome, and .formal.complete all record a successful terminal state. The higher-level terminal evidence resolves that minor inconsistency.

Evidence availability. The record includes the raw JSONL trajectory, task source, complete candidate patch, control records and corrections for all formal attempts, the valid attempt’s training and validation receipts, and three final summaries plus completion markers. A second training seed, a public64 proxy evaluation of the 40-epoch candidate, a public64 aesthetic score for the fixed start, and a clean causal ablation of the changed factors are not available. This report preserves those gaps rather than inferring missing results.

2. Reader-facing overview

The task asks for improved aesthetic alignment from a fixed Stable Diffusion v1.5 start on one accelerator. The shipped solution is already a functioning Denoising Diffusion Policy Optimization (DDPO) trainer. It trains only Low-Rank Adaptation (LoRA) weights, generates images on policy from a small animal-prompt distribution, applies a fixed aesthetic reward model, and updates the diffusion policy with a clipped objective analogous to Proximal Policy Optimization. The submitted candidate introduces no new reward, data source, or update equation. Its only training-semantic change is extending the outer schedule from 13 to 40 epochs.

The central exploration observation was a monotonic proxy sequence under the same training seed and public64 generation stream: the unmodified recipe scored 5.498931 after 4 epochs, 5.542108 after 8, and 5.594094 after 13. A joint sampling/normalization/clip change, a two-inner-pass variant, and a larger sampling batch all failed to beat the matched four-epoch control. The agent therefore selected “continue the original trajectory” and extrapolated it to 40 epochs. The direction has evidence, but 40 epochs itself was never tested during exploration, and every training experiment used seed 43.

The valid formal replay did complete 40 epochs. On the hidden 256-example final protocol, progress 37, 38, and 39 scored 5.617797 ± 0.024913, 5.617847 ± 0.024829, and 5.615683 ± 0.025302, respectively; progress 38 is the official best. It exceeds the shipped 13-epoch reference under the same final protocol by 0.091474 and the fixed start by 0.220536. The last three artifacts are effectively on a plateau, and the valid formal run used only 9,833 seconds, or 22.8% of the 12-hour allowance. The defensible conclusion is therefore that a longer unchanged schedule helped in this single-seed run, not that the trajectory established a generally superior algorithm or an optimal 40-epoch schedule. No agent access to the hidden final stream, external training input, or lineage corruption was found. The two invalid formal runs instead expose a confirmed platform resource-isolation defect that is separate from agent conduct.

3. Task, baseline, and evaluation contract

3.1 Task and fixed boundaries

The start model is fixed to runwayml/stable-diffusion-v1-5 revision pinned private revision. The aesthetic reward uses openai/clip-vit-large-patch14 revision pinned private revision, and the upstream DDPO source is pinned to commit pinned private revision. Training is restricted to one physical accelerator and a no-network mode. The candidate workspace is editable, while the scorer runs independently from a frozen harness. Training prompts are sampled on policy from a 45-entry simple_animals list embedded in source; the task mounts no external training dataset.

The proxy metric is mean_aesthetic_score_public64, maximized over 64 generated images on a public generation protocol. Each reported uncertainty is the within-row standard error of those image scores. The official metric is mean_aesthetic_score_final256, also maximized, but it evaluates 256 images from a held-out prompt and latent stream mounted only during scoring. The final results cover 44 distinct prompts. The protocols differ in sample count and prompt/latent random stream, so proxy and final scores are not directly subtractable. CLIP alignment and mean pairwise CLIP distance are auxiliary diagnostics with no pass/fail gate.

Exploration allows 14,400 seconds. Formal replay allows 43,200 seconds; the runner environment separately records a 42,600-second maximum wall-clock variable and a 300-second reserve. The task package publishes same-final-protocol reference scores of 5.397311 for the fixed start and 5.526373 for the shipped 13-epoch solution. That shipped reference used 3,340.09 seconds for training, 528.28 seconds for scoring, and 58,598 MiB of peak memory. It too is a stochastic single run and does not supply across-seed uncertainty.

3.2 How the baseline works

The baseline runs seed 43 for 13 outer epochs. Each epoch generates 32 on-policy trajectories as a sampling batch of eight repeated over four batches. An image follows 50 DDIM denoising transitions with classifier-free guidance 5.0. A five-layer multilayer perceptron maps normalized CLIP-L/14 image embeddings to aesthetic rewards. The trainer maintains per-prompt reward statistics with a buffer of 32 and a minimum of 16 observations, falling back to current-batch global normalization when a prompt lacks enough history. Advantages are then clipped to ±5.

Only LoRA parameters in the U-Net attention processors are trainable; the upstream default rank is four. The exponential of the new-minus-old denoising transition log probability produces the importance ratio. A PPO-like surrogate uses the worse of the unclipped and clipped negative objectives with ratio clip 0.0001. The train batch is four with four-way gradient accumulation, for an effective batch of 16. One inner epoch yields two gradient updates per outer epoch. The learning rate is 3×10^-4, maximum gradient norm is one, and computation uses fp16. In compact form, the flow is: prompt → 50-step on-policy denoising trajectory and old log probabilities → aesthetic reward → per-prompt or fallback global normalization and advantage clipping → clipped policy objective → LoRA update.

The trainer attempts a periodic save every outer epoch, while upstream retention keeps only the latest three checkpoints simultaneously. The wrapper exports the latest loadable LoRA and publishes the three currently retained checkpoints under numeric progress names. Cumulative save attempts and simultaneously retained or published artifacts are therefore different counts.

4. Four-hour exploration and decision process

In trajectory order, the agent first read the task, trainer, and scorer and established an unmodified four-epoch control. It then screened a joint throughput variant, completed eight- and thirteen-epoch length checks, and used the later phase for a two-inner-pass counterexample, a larger sampling batch, frozen load validation, knob reversion, patch inspection, and formal-recipe preparation. The final portion ended in cleanup and submission rather than a second training seed.

U-01 - Establish a matched baseline and diagnose update constraints

Motivation and hypothesis. The agent hypothesized that the exceptionally narrow 0.0001 ratio clip might constrain learning and that per-prompt statistics were “mostly inactive” during short training. A matched control was needed for later changes.

Concrete change and experimental setup. No baseline hyperparameter changed. The recipe trained for four epochs under seed 43 and was evaluated by the maximized 64-image public64 proxy.

Observed result. Runtime was 1,302.749 seconds; public64 was 5.498930871 ± 0.042569767, CLIP alignment 0.236080379, and mean pairwise CLIP distance 0.338768601. Three retained progress checkpoints were discovered, and the latest export was loadable.

Agent interpretation. The agent treated this as a healthy short-run baseline and an end-to-end training/export/evaluation control, then continued testing clipping, normalization, and schedule length.

Report assessment and confounds. The narrow-clip and inactive-statistics claims remained hypotheses. With 45 prompts and persistent buffers, per-prompt statistics can become active later. A fixed-start public64 aesthetic mean is not available because the proxy reference contains only auxiliary base diagnostics; no fixed-start proxy delta can be computed.

Decision and consequence. Adopted as the matched four-epoch control, with no direct candidate change.