SkillDuet: Internalizing Skills through Agent–Environment Dual Harnesses

Skills are reusable strategies distilled from prior interactions that guide agents in task execution. Although skills improve inference-time performance, they do not become intrinsic agent capabilities, motivating skill internalization for autonomous execution without external guidance. However, existing methods typically rely on start-of-trajectory skill guidance, whose influence may gradually diminish over long interaction. We propose SkillDuet, a novel skill-internalization framework that can deliver in-trajectory guidance, providing persistent supervision at relevant decision points. To realize such supervision, the core of SkillDuet is agent–environment dual harnesses: beyond the start-of-trajectory guidance provided by an agent harness, an environment harness augments observations with skill-derived feedback to provide in-trajectory guidance. We further design Paired Trajectory Internalization procedure, which collects paired assisted and unassisted trajectories, then converts successful assisted trajectories into skill-free supervision by removing external guidance. It internalizes skill-guided behavior through SFT with task-benefit weighting on converted trajectories, while using RL on unassisted trajectories to reinforce autonomous execution. Furthermore, to evaluate skill internalization in challenging long-horizon tasks, we introduce Hard-ALFWorld, an ALFWorld variant with multiple goals, same-type distractors, and scene-grounded object references. Experiments on Hard-ALFWorld and Search-QA with two backbones demonstrate that SkillDuet achieves leading performance in most settings and remains competitive across all evaluations.