Rethinking Self-Evolving Agent Skills: Feedback Dynamics over Multiple Rounds
Self-evolving skill systems promise to improve agents by turning execution feedback into persistent skill updates without changing the underlying model. Yet it remains unclear when further evolution helps, how successful and failed trajectories shape revision, and whether extra test-time computation can recover the same gains. To address these questions, we present a controlled evaluation framework across five benchmarks and three models. Our primary study contains 42 feedback runs across 14 supported model-benchmark settings. Within each setting, we hold the executor and optimizer configuration, revision procedure, validation rule, and round budget fixed, while varying only the feedback ...
| Authors | Yuxuan Liu, Zhaochen Su, Yuhao Zhang et al. |
|---|---|
| Primary category | cs.SE |
| Published | 31 July 2026 |
| Last updated at source | 31 July 2026 |
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