Skill is Not One-Size-Fits-All: Model-Aware Skill Alignment for LLM Agents
This work improves the performance of LLM agents on long-horizon interactive tasks by adapting external skills to specific LLM backbones, which is an incremental improvement for researchers and developers working with LLM agents.
This paper addresses the problem of skill effectiveness being model-dependent for LLM agents, where skills beneficial for one model can harm another. They propose MASA, a framework that adapts skills to specific backbones, achieving performance gains of up to 25.8 points over the strongest baseline across three interactive environments and four backbones.
LLM agents increasingly retrieve externally curated skills-procedural instructions retrieved at decision time-to improve performance on long-horizon interactive tasks. Existing skill libraries are typically treated as model-agnostic, reusing the same skill formulations across backbones with substantially different capacities and behaviors. However, our controlled experiments across multiple model scales show that skill effectiveness is strongly model-dependent: a skill that benefits one backbone can harm another. Motivated by this observation, we propose MASA Model-Aware Skill Alignment, a framework that adapts skills to each target backbone without modifying agent weights. MASA operates in two stages: (1) a hierarchical skill evolution pipeline that iteratively rewrites general and task-specific skills using hill climbing and UCB-driven tree search, guided by environment feedback and model capability profiles; and (2) a lightweight model-conditioned skill rewriter trained on evolution trajectories to reproduce the adaptation in a single forward pass. Experiments across three interactive environments and four backbones show that MASA consistently achieves the best overall performance, with gains of up to 25.8 points over the strongest baseline. The learned rewriter further generalizes to unseen tasks and environments without additional search, consistently outperforming a much larger teacher LLM at a fraction of the inference cost.