1/ Recursive self-improvement (RSI) depends on agents improving how AI systems are trained —not just tuning hyperparameters, but improving the training algorithm itself.

We tested this directly with AI4AI-Bench: 10 real research repositories spanning 10 distinct algorithm families.

Full breakdown GitHub: [https://github.com/Einsia/AI4AI-Bench…] Paper Link: [https://arxiv.org/pdf/2608.20318] Einsia Website:[https://lab.einsia.ai/ai4ai/]

The results: The average score is just 0.166. Even the best-performing model, Opus 5, reaches only 0.288. The median exploration cost per task rises from $1.69 to $34.60.

#AI4AI #RecursiveSelfImprovement #AIResearch #AI4AI_Bench

Einsia/AI4AI-Bench

Source: https://github.com/Einsia/AI4AI-Bench

AI4AI-Bench

AI4AI-Bench asks whether a coding agent can improve an existing AI training recipe—not merely edit code that passes a fixed test. Its ten tasks span generation, alignment, reasoning, unlearning, pruning, reinforcement learning, reward modeling, and model merging.

Each run separates open-ended experimentation from reproducible measurement. The agent may explore for up to four hours, but only a source patch crosses into a fresh formal environment; formal training then runs for up to twelve hours, publishes at most three checkpoints, and hands each checkpoint to frozen validation and final evaluation.

fixed task + assets
        │
        ├─ 4 h Explore ──> candidate.patch
        │                         │
        └──────── fresh Formal <──┘
                  (up to 12 h)
                         │
             up to 3 checkpoints
                         │
             validation ──> final score

Quickstart

AI4AI-Bench targets Linux amd64, Python 3.10+, Docker with NVIDIA Container Toolkit, and an NVIDIA GPU. Official runs use one B300; other GPUs are useful for local development when the selected task fits. Install a native Codex or Claude CLI before starting an agent run.

python3 -m venv .venv
. .venv/bin/activate
python -m pip install -e '.[assets]'

Prepare one task’s pinned public assets. Set HF_TOKEN first if an upstream repository is gated.

export AI4AI_ASSET_STORE=/data/ai4ai/assets
python tools/prepare_assets.py \
  --task ddpo_sd15_aesthetic \
  --assets "$AI4AI_ASSET_STORE/ddpo_sd15_aesthetic" \
  --execute
python tools/verify_assets.py \
  --task ddpo_sd15_aesthetic \
  --assets "$AI4AI_ASSET_STORE/ddpo_sd15_aesthetic" \
  --hash

Run the no-API GPU smoke test first. It pulls one published image if needed and exercises Docker, GPU passthrough, a CUDA kernel, host mounts, and the mock score lifecycle.

bash tools/smoke.sh --root /data/ai4ai/smoke --gpu 0

Then configure the selected Agent and check the complete host without displaying credentials.

export OPENAI_API_KEY=your-key
python tools/check_setup.py \
  --task ddpo_sd15_aesthetic \
  --assets "$AI4AI_ASSET_STORE/ddpo_sd15_aesthetic" \
  --root /data/ai4ai/runs \
  --gpu 0 --agent codex --mode local

Start a real run. The default lifecycle performs Explore, fresh formal retraining, checkpoint validation, and final evaluation.

bash orchestrator/trial.sh ddpo-codex \
  --task tasks/ddpo_sd15_aesthetic \
  --assets "$AI4AI_ASSET_STORE/ddpo_sd15_aesthetic" \
  --root /data/ai4ai/runs --gpu 0 \
  --agent codex --model gpt-5.6-sol --reasoning-effort high

For Claude, use --agent claude --model claude-opus-5 and set ANTHROPIC_API_KEY (or ANTHROPIC_AUTH_TOKEN). See the runtime guide for alternate paths, endpoints, local/official verification modes, and storage requirements.

Evaluation and replay

Replay an existing source patch from the same fixed start:

bash orchestrator/trial.sh replay-name \
  --task tasks/ddpo_sd15_aesthetic \
  --assets /data/ai4ai/assets/ddpo_sd15_aesthetic \
  --root /data/ai4ai/runs --gpu 0 \
  --candidate-patch candidate.patch

Evaluate one to three existing checkpoints independently:

bash orchestrator/evaluate.sh eval-name \
  --task tasks/ddpo_sd15_aesthetic \
  --assets /data/ai4ai/assets/ddpo_sd15_aesthetic \
  --root /data/ai4ai/evaluations --gpu 0 \
  --checkpoint 1000=/path/to/checkpoint-1000

These are self-hosted final evaluations. The public warn defaults label their receipts as non-official local results; strict official-mode requirements are documented separately. See evaluation and receipts for checkpoint selection, result states, and the current absence of a blind evaluation service.

Tasks and documentation

• Ten benchmark tasks
• Runtime assets
• Runtime, storage, and mounts
• Evaluation and receipts
• Published task images
• Troubleshooting

Citation

@article{chi2026ai4ai,
  title   = {AI4AI-Bench: Benchmarking LLM Agents in Algorithmic Design for Recursive Self-Improvement},
  author  = {Yizhe Chi and Wenyi Li and Deyao Hong and Xiaoqiu Wang and Mingju Gao and Kaisen Yang and Bingxiang He and Youjie Zheng and Calvin Xiao and Qinhuai Na},
  journal = {arXiv preprint arXiv:2608.20318},
  year    = {2026}
}

License

The benchmark code is released under Apache-2.0. Models, datasets, and image contents remain subject to their upstream terms; see third-party notices.