Capability Loss in Embodied Recursive Self-Improvement

Recursive self-improvement (RSI) is emerging as a promising method to improve embodied agents with their own experience. However, we argue that, under a fixed task distribution, RSI can erode tail and error-recovery capabilities while nominal success keeps rising, a failure we call silent erosion. We study this failure with a dynamical model over behavior modes and identify two causes of this capability loss: selection, which down-weights the modes a policy performs poorly on, and visitation drift, which removes the post-failure states in which recovery is exercised. We turn this analysis into RESCUE, which spends a fixed external budget on mastered modes with shrinking data mass, retains them across generations, and forecasts degradation from their coverage. Our experiments separate data recursion from parameter drift, intervene on each mechanism, and replicate across policy architectures, benchmarks, and real-robot deployment. Our code will be publicly available.