Skill is Not One-Size-Fits-All: Model-Aware Skill Alignment for LLM Agents

• URL: http://arxiv.org/abs/2605.30723v1
• Date: Fri, 29 May 2026 01:34:42 GMT
• Title: Skill is Not One-Size-Fits-All: Model-Aware Skill Alignment for LLM Agents
• Abstract summary: LLM agents increasingly retrieve externally curated skills-procedural instructions retrieved at decision time to improve performance on long-horizon interactive tasks.<n>We propose MASA Model-Aware Skill Alignment, a framework that adapts skills to each target backbone without modifying agent weights.<n> MASA consistently achieves the best overall performance, with gains of up to 25.8 points over the strongest baseline.
• Score: 9.144810798419975
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Evo-Harness: Context-to-Harness Skill Compilation for Self-Evolving Agents [67.97365535829098]
  Learning from experience is critical for developing capable, self-improving large language model (LLM) agents.<n>We propose Evo-Harness, which distills noisy, single-shot executions into reusable skill harnesses for cross-domain and topic-level adaptation.<n>Our analysis demonstrates the effectiveness of Evo-Harness and provides a principled understanding of how LLM agents can effectively learn on the fly.
  arXiv  Detail & Related papers  (2026-08-15T06:43:56Z)
• Bidirectional Context Self-Distillation for Reinforcement Learning of Skill-Based LLM Agents [11.967142343676171]
  We propose BCSD (Bi Context Self-Distillation), a framework that combines self-distillation and reinforcement learning to train agents to use external skills more effectively.<n> Experiments on ALFWorld and WebShop demonstrate that BCSD achieves the strongest overall performance across model scales.
  arXiv  Detail & Related papers  (2026-08-10T12:53:06Z)
• Rethinking Self-Evolution: A Constrained Exploration-Exploitation Process for Mitigating Skill Overfitting [20.811638832981178]
  Large language model (LLM) agents need to accumulate and reuse experience from past interactions.<n>Data-driven skill optimization is prone to overfitting to the limited trajectories collected from real environments.<n>We propose SkillBoost, a three-stage framework that mitigates both risks.
  arXiv  Detail & Related papers  (2026-07-29T09:05:40Z)
• Skill-MAS: Evolving Meta-Skill for Automatic Multi-Agent Systems [16.25298848475875]
  Large Language Model (LLM)-based automatic Multi-Agent Systems (MAS) generation has become a crucial frontier for tackling complex tasks.<n>Inference-time MAS leverages frozen frontier LLMs but repeats identical searches without learning from past experience.<n>Training-time MAS internalizes experience via gradient updates but is constrained by the low capability ceiling of smaller models.<n>We propose Skill-MAS, a novel third path that decouples experience retention from parametric updates by conceptualizing the high-level orchestration capability as an evolvable Meta-Skill.
  arXiv  Detail & Related papers  (2026-06-17T09:12:50Z)
• SkillComposer: Learning to Evolve Agent Skills for Specification and Generalization [99.18305770138677]
  Agent skills are reusable strategies that guide agent reasoning and action.<n>Current skill construction methods treat the problem as one-shot extraction.<n>We introduce SkillComposer, a framework that decomposes skill construction into three learnable operations.
  arXiv  Detail & Related papers  (2026-06-04T12:16:08Z)
• Skill-R1: Agent Skill Evolution via Reinforcement Learning [84.35984979949502]
  Skill-R1 is a reinforcement learning framework for instance-level recurrent skill optimization from verifiable rewards.<n>It preserves black-box compatibility with both open- and closed-source models while making adaptation substantially cheaper than model-level updates.
  arXiv  Detail & Related papers  (2026-05-10T06:19:15Z)
• ARISE: Agent Reasoning with Intrinsic Skill Evolution in Hierarchical Reinforcement Learning [17.98540130851038]
  ARISE (Agent Reasoning via Intrinsic Skill Evolution) is a hierarchical reinforcement learning framework.<n>It uses a shared policy to manage skills at high-level and to generate responses at low-level.<n>A hierarchical reward design guides the co-evolution of reasoning ability and library quality.
  arXiv  Detail & Related papers  (2026-03-17T02:03:17Z)
• SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning [83.98129545309277]
  We propose SkillRL, a framework that bridges the gap between raw experience and policy improvement.<n>Our approach introduces an experience-based distillation mechanism to build a hierarchical skill library SkillBank.<n> Experimental results on ALF, WebShop and seven search-augmented tasks demonstrate that SkillRL achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-02-09T03:17:17Z)
• Reinforcement Learning for Self-Improving Agent with Skill Library [14.717149089634718]
  Large Language Model (LLM)-based agents have demonstrated remarkable capabilities in complex reasoning and multi-turn interactions.<n>One promising approach is implementing skill libraries that allow agents to learn, validate, and apply new skills.<n>We propose a Reinforcement Learning (RL)-based approach to enhance agents' self-improvement capabilities with a skill library.
  arXiv  Detail & Related papers  (2025-12-18T21:58:19Z)
• BOFA: Bridge-Layer Orthogonal Low-Rank Fusion for CLIP-Based Class-Incremental Learning [84.56022893225422]
  Class-Incremental Learning (CIL) aims to continually learn new categories without forgetting previously acquired knowledge.<n>Applying vision-language models such as CLIP to CIL poses two major challenges: (1) adapting to downstream tasks often requires additional learnable modules, increasing model complexity and susceptibility to forgetting; and (2) while multi-modal representations offer complementary strengths, existing methods have yet to fully realize their potential in effectively integrating visual and textual modalities.
  arXiv  Detail & Related papers  (2025-11-14T15:51:40Z)
• SkillVerse : Assessing and Enhancing LLMs with Tree Evaluation [70.27631454256024]
  SkillVerse is an unsupervised tree-structured diagnosis framework for understanding model proficiency in specific abilities.<n>Given proficiency at arbitrary levels of granularity, SkillVerse is flexible to produce insights of behaviors of modern large models.
  arXiv  Detail & Related papers  (2025-05-31T00:08:59Z)
• Learning Adaptive Dexterous Grasping from Single Demonstrations [27.806856958659054]
  This work tackles two key challenges: efficient skill acquisition from limited human demonstrations and context-driven skill selection.<n>AdaDexGrasp learns a library of grasping skills from a single human demonstration per skill and selects the most suitable one using a vision-language model (VLM)<n>We evaluate AdaDexGrasp in both simulation and real-world settings, showing that our approach significantly improves RL efficiency and enables learning human-like grasp strategies across varied object configurations.
  arXiv  Detail & Related papers  (2025-03-26T04:05:50Z)
• CLIPood: Generalizing CLIP to Out-of-Distributions [73.86353105017076]
  Contrastive language-image pre-training (CLIP) models have shown impressive zero-shot ability, but the further adaptation of CLIP on downstream tasks undesirably degrades OOD performances. We propose CLIPood, a fine-tuning method that can adapt CLIP models to OOD situations where both domain shifts and open classes may occur on unseen test data. Experiments on diverse datasets with different OOD scenarios show that CLIPood consistently outperforms existing generalization techniques.
  arXiv  Detail & Related papers  (2023-02-02T04:27:54Z)
• Hierarchical Few-Shot Imitation with Skill Transition Models [66.81252581083199]
  Few-shot Imitation with Skill Transition Models (FIST) is an algorithm that extracts skills from offline data and utilizes them to generalize to unseen tasks. We show that FIST is capable of generalizing to new tasks and substantially outperforms prior baselines in navigation experiments.
  arXiv  Detail & Related papers  (2021-07-19T15:56:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.