AI

UniSkill: Learning Actor-Aligned Skill Proposals for an Evolving Policy

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Briefings

UniSkill enables large language model agents to propose skill edits that improve performance without requiring costly new actor rollouts. The method uses a shared policy to interact with the environment and measures action log-likelihood gaps between successful and failed trajectories to guide skill proposals. Empirically, UniSkill achieves 98.4% success on ALFWorld and 84.7% on WebShop while maintaining stable joint training.

Key points

• UniSkill uses a shared policy to interact with the environment and propose skill edits (Add, Update, or No Edit) from resulting trajectories.
• The method measures action log-likelihood gaps between successful and failed trajectories to guide skill proposals, avoiding costly new rollouts.

Context

Large language model agents improve by retaining reusable skills distilled from prior interactions. Recent work optimizes task execution and skill extraction, but testing each proposed skill requires costly additional actor rollouts. UniSkill addresses this by using contrastive action feedback to measure how replacing a retrieved skill with a proposed skill changes the actor's action log-likelihood gap.

Limitations

Evidence is limited to ALFWorld and WebShop benchmarks. Performance on smaller backbones is shown but not fully quantified. Implementation details and specific training parameters are not disclosed.

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