OpenRSI Foundation
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- OpenRSI Index benchmarks whether AI agents can improve real ML research
OpenRSI Index, launched by the OpenRSI Foundation, has drawn attention on X, where its announcement post has passed 133,000 views. The benchmark draws tasks from real open-source research projects such as Marin, Qwen and GPIC, handing agents existing models, code, data and training recipes to improve, spanning pretraining, post-training and image generation, with the site listing representative single runs at roughly 5,815 to 20,864 H100-hours. Evaluation uses an RSI-Harness that separates the agent's workspace from a clean judging environment, pausing and snapshotting the workspace for scoring; advisers include Luke Zettlemoyer, Karthik Narasimhan, Yejin Choi and Dawn Song, and a companion RSI-Anything pipeline lets researchers turn their own open problems into runnable RSI tasks.
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