Policy updates reveal when skills become less useful in AI agents
Before the Next Rollout: Diagnosing Skill Utility Shifts from Policy Updates
Artificial Intelligence
Summary
When AI agents learn, they use different skills to decide actions. Sometimes, updating the agent's decision strategy changes how useful each skill really is, even if the skill itself hasn't changed. The authors show a way to spot which skills have become less helpful after these updates, without needing extra tests. This helps focus improvement efforts on skills that matter most under the new strategy.
What this means in practice
- •For reinforcement learning engineers: Prioritize improvement on agent skills whose usefulness has declined after policy updates using existing rollout data alone.
- •For ai model developers: Diagnose and adjust skill contributions in updated policies to achieve better agent behavior with reduced computational overhead.
Authors
Yifan Wang, Zhenyi Gai, Yiwen Zhao, Mingxuan Jiang, Keyang Chen, Zeping Li, Hongfeng Chai, Yixin Cao
Abstract
Skill-evolution methods typically analyze execution trajectories to guide agent skill refinement. However, policy updates can change the behavioral effects and marginal utility of an unchanged skill. Attribution based on earlier trajectories may therefore no longer reflect the skill's contribution under the updated policy. Moreover, trajectory failures can arise from multiple factors, making individual skill contributions difficult to isolate. We evaluate whether policy-update signals contain predictive information about changes in skill marginal utility. At fixed contexts from pre-update training rollouts, we measure how policy updates change the contrast in token preferences between skill and control conditions. Combining these interaction shifts with advantage estimates yields reward-directed readouts for diagnosing utility changes. The readouts reuse existing training rollouts and require only forward passes for single-step action scoring, without additional post-update rollout evaluation. Our experiments show that policy updates can alter skill marginal utility and that policy-update readouts carry diagnostic information for identifying skills with declining utility. Integrating readout-guided prioritization into skill evolution improves downstream performance while reducing policy and editor token consumption.