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Introducing RSI-Anything: Unlocking Recursive Self-Improvement with World Models š Can AI learn to improve itself by predicting the consequā¦
Introducing RSI-Anything: Unlocking Recursive Self-Improvement with World Models š Can AI learn to improve itself by predicting the consequences of its actions? Built on JEPA-Anything, RSI-Anything us
Introducing RSI-Anything: Unlocking Recursive Self-Improvement with World Models š Can AI learn to improve itself by predicting the consequences of its actions? Built on JEPA-Anything, RSI-Anything uses world models to guide action selection and learning. Feedback improves the policy or workflow and, when needed, the world model. Validated updates carry forward to shape the next round of experience. Across code, GUI interaction, laboratory planning, and reaction optimization: ā¢ Code-repair success: 26.3% ā 31.2%, with the coder fixed ā¢ GUI agent task: 39.4% fewer actions, with higher completion ā¢ Lab-planning scores: 21.80% ā 46.01% over five policy versions ā¢ Mean best observed reaction yield: 96.15% vs. 82.16% for Bayesian optimization, under the same 40-experiment budget š Paper: https://arxiv.org/abs/2610.13912 š» Code: https://github.com/Gen-Verse/RSI-Anything Watch the video below š #RSI #WorldModels #RSI #AIAgents #AIforScience #JEPA Media
Source: Yann LeCun (X) | 2026-10-12