RSI Index (OpenAI)
An aggregate of OpenAI's internal evaluations of progress toward recursive self-improvement. The parts are real AI research tasks such as debugging research systems, optimizing kernels and training recipes, and improving another model.
The GPT-5.6 post reports Sol 57.9% against GPT-5.5 41.7%. Its table lists Internal Research Debugging, KernelGen 1P, NanoGPT and PostTrainBench Lite beside it. Do not confuse it with the Vals RSI Index.
- Made by
- OpenAI
- Released
- July 2026
- Kind
- Labs' internal R&D tests
- Level
- Research frontier
- Scored
- Checked automatically
- Answers
- Agent task
- Tasks
- Kept private
No scores here yet
No lab, leaderboard or Epoch AI run has reported a score for RSI Index (OpenAI) yet.
No lab model card reports it yet
We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark.
More in AI research
- PaperBench In use Agent replicates ICML 2024 papers from scratch. LLM judge grades against author-written hierarchical rubrics.
Qwen 3.8 Max 93% - MLS-Bench In use Agent must invent one algorithmic change in an ML research scaffold whose gain transfers across settings. Scored relative to human baselines. Claude Fable 5.1 50.3%
- PostTrainBench 1.2 In use CLI agent gets a base model, one H100 and a time budget to post-train it for a target benchmark. Scored on normalized gain. Claude Fable 5.1 44.6%
- MLE-bench In use Kaggle-style ML engineering competitions. Agent trains models and is scored against competition leaderboards. GPT-6 Astra 93.8%
- KernelBench In use Replace PyTorch operators with faster custom GPU kernels. Scored on correctness and speedup.
MiniMax-M3 28.8% - PMPP-Hard In use Agents write GPU kernels for 69 tasks that must be correct, and on 48 of them also run within 1.25 times the reference time. The score is the area under the solved-tasks curve over a 10-hour budget, out of 690 task-hours.
Inkling 2 Small 494.3