Learning from Revision Consequences: Hindsight Meta-Experience Distillation for Self-Improving Agents
Read the original on arXiv AI →The paper introduces Hindsight Meta-Experience Distillation (HMED), a method that lets self‑improving agents evaluate how revisions to their Meta‑Skills affect skill discovery by re‑executing both the original and revised Meta‑Skills from the same initial state. By comparing these outcomes, HMED creates structured Meta‑Experiences that provide learning signals even for revisions that are not ultimately kept. Experiments on three interactive benchmarks show that HMED consistently outperforms strong baselines, improving skill discovery by focusing on the consequences of the improvement process rather than just branch outcomes.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.