SkillEvo: Self-Renewing Evolution Gradients from Multi-Turn Interaction Feedback
Summary
SkillEvo introduces a method to continuously improve AI agent skills by using multi-turn interaction feedback and governance layers to maintain evolution gradients, surpassing self-reflection and single-turn QA-driven approaches.
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Paper page - SkillEvo: Self-Renewing Evolution Gradients from Multi-Turn Interaction Feedback
Source: https://huggingface.co/papers/2608.13120
Abstract
SkillEvo improves agent skills through multi-turn feedback and active governance to sustain evolution gradients.
Agent Skills are today either hand-authored or produced in a single LLM generation pass, and consequently possess no closed loop through which they might improve from the interaction failures they actually cause. Recent work does close this loop, but derives its feedback from single-turn question-answering evaluation. The consequence is a sharp asymmetry: once the first round has patched the gaps that a single exchange can reveal, the evolution gradient decays, the defects that surface only across multiple turns remain invisible, and evolution stalls. Governance in these systems is likewise driven by an end-to-end verification score, a scalar gate that can reject a degraded candidate but can neither localize nor repair its structural cause. We argue that the binding constraint on sustained skill evolution is neither editing capability nor the number of iterations, but whether the evaluation feedback keeps supplying trustworthyevolution gradients. We introduce SkillEvo, in which trustworthy feedback generates the gradient and controllable governance constrains its direction. The first component recastsmulti-turn user simulationfrom an evaluation endpoint into afeedback generator: follow-up questions expose defects layer by layer, so that every round of revision both consumes feedback and produces new feedback. The second replaces the passive rejection of a scalar gate with an independentgovernance layerthat actively repairs factual degradation and structural bloat, preventing the gradient from drifting as degradation accumulates. Across six categories of cloud services, 9 production Skills, and 98 skill-reference files, SkillEvo surpassesself-reflection-based evolution by 23.0 points and single- turn-QA-driven evolution by 15.4 points.
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