OtherarXiv 2610.13912
RSI-Anything: Unlocking Recursive Self-Improvement with World Models
Proposes RSI-Anything, which uses a world model to select candidates and retain policy updates.
- arXiv
- 2610.13912
- Published
- Layer
- Application layer
- Setting
- coding agent, Browser and computer use
- Evaluated models
- ReasonFlux-Coder-7B, 4B policy, W1
AbstractRSI-Anything integrates world models into recursive self-improvement through prediction, feedback, and retention checks, and reports gains across multiple domains.
RSI-Anything is a cross-domain framework: it uses world model predictions to decide which candidates to take external feedback for, then uses the feedback to decide whether the policy, workflow, or predictor is updated, and uses checks to decide which pair is retained. The difficulty to address is the limited interaction budget, and the fact that once the policy changes, the candidate distribution faced by the predictor also changes. The authors argue that improving only one side does not guarantee that the assembled system will be better.