Large language model agents increasingly rely on natural-language skills to solve complex tool-use tasks. However, these tasks often admit more than one valid solution path, which complicates skill improvement. The paper argues that forcing failed trajectories to match a single reference path is inappropriate when multiple valid paths exist.
Instead, the work introduces a deviation-guided skill self-evolution method that treats wrong turns as learning signals. By using deviations to guide evolution, the approach aims to help agents refine their skills without discarding useful alternative strategies.
The abstract does not include experimental details, so the reported benefits are not yet independently verified. The contribution is primarily conceptual: reframing deviations as informative rather than erroneous.