SkillGraph: Skill-Augmented Reinforcement Learning for Agents via Evolving Skill Graphs

• URL: http://arxiv.org/abs/2605.12039v1
• Date: Tue, 12 May 2026 12:21:49 GMT
• Title: SkillGraph: Skill-Augmented Reinforcement Learning for Agents via Evolving Skill Graphs
• Abstract summary: We propose SKILLGRAPH, a framework that represents reusable skills as nodes in a directed graph.<n>SKILLGRAPH retrieves not just individual skills, but an ordered skill subgraph that can guide multi-step decision making.<n> Experiments show that SKILLGRAPH achieves state-of-the-art performance against memory-augmented RL methods.
• Score: 70.1970574147839
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Skill libraries enable large language model agents to reuse experience from past interactions, but most existing libraries store skills as isolated entries and retrieve them only by semantic similarity. This leads to two key challenges for compositional tasks. Firstly, an agent must identify not only relevant skills but also how they depend on and build upon each other. Secondly, it also makes library maintenance difficult, since the system lacks structural cues for deciding when skills should be merged, split, or removed. We propose SKILLGRAPH, a framework that represents reusable skills as nodes in a directed graph, with typed edges encoding prerequisite, enhancement, and co-occurrence relations. Given a new task, SKILLGRAPH retrieves not just individual skills, but an ordered skill subgraph that can guide multi-step decision making. The graph is continuously updated from agent trajectories and reinforcement learning feedback, allowing both the skill library and the agent policy to improve together. Experiments on ALFWorld, WebShop, and seven search-augmented QA tasks show that SKILLGRAPH achieves state-of-the-art performance against memory-augmented RL methods, with especially large gains on complex tasks that require composing multiple skills.

Related papers

• Break It Down, Pass It On: Cross-Task Skill Transfer in LLM Agents [17.291924088593408]
  Large language model (LLM) agents can induce skills from completed tasks and reuse them later to grow more capable with experience.<n>In practice, induced skills may transfer unreliably and can even harm the agent that retrieves them.<n>We conduct a comprehensive and controlled study of how the way skills are induced shapes their transfer across tasks.
  arXiv  Detail & Related papers  (2026-08-20T17:12:08Z)
• SkillTrace: Traversing a Query-Skill Graph for Composable LLM Agents [22.759579282367614]
  We introduce SkillTrace, which organizes user queries into a semantic hierarchy, matches skill queries and candidates, and propagates over the skill dependencies.<n> Experiments on SkillsBench and ALFWorld demonstrate that SkillTrace achieves state-of-the-art performance, reaching a success rate of 53.17%.
  arXiv  Detail & Related papers  (2026-08-03T15:07:11Z)
• Generative Skill Composition for LLM Agents [58.47191648555256]
  LLM agents benefit from skills for solving complex tasks.<n>As skill libraries grow, selecting an appropriate skill composition has emerged as a central bottleneck.<n>We propose SkillComposer, which instantiates structured skill composition as task-conditioned skill sequence prediction.
  arXiv  Detail & Related papers  (2026-06-30T17:53:09Z)
• 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)
• COLLEAGUE.SKILL: Automated AI Skill Generation via Expert Knowledge Distillation [58.84646485020439]
  We present an automated trace-to-skill distillation system for generating person-grounded AI skills.<n> COLLEAGUE.SKILL produces a versioned skill package with two tracks: a capability track for practices, mental models, and decisions, and a bounded behavior track for communication style, interaction rules, and correction history.
  arXiv  Detail & Related papers  (2026-05-29T12:59:08Z)
• SkillRAE: Agent Skill-Based Context Compilation for Retrieval-Augmented Execution [10.83969404435554]
  Large Language Model (LLM)-based agents increasingly rely on reusable skill libraries to solve artifact-rich tasks.<n>We propose SkillRAE, a two-stage RAE approach focusing on skill-based context compilation.<n> Experiments on two public benchmarks show that SkillRAE achieves a significant improvement over baselines for RAE.
  arXiv  Detail & Related papers  (2026-05-11T07:31:48Z)
• SkillGen: Verified Inference-Time Agent Skill Synthesis [60.927977774369516]
  SkillGen is a framework that synthesizes a single auditable skill from trajectories generated by a base agent.<n>It identifies reusable success patterns, recurring failure modes, and behaviors that appear in nearby successes but are missing from failures.<n>A key novelty in SkillGen is that we model agent skills as interventions to empirically verify the net effect of skills on the overall performance.
  arXiv  Detail & Related papers  (2026-05-09T19:24:11Z)
• SkillOS: Learning Skill Curation for Self-Evolving Agents [67.94374107466957]
  We propose SkillOS, an experience-driven RL training recipe for learning skill curation in self-evolving agents.<n>SkillOS pairs a frozen agent executor that retrieves and applies skills with a trainable skill curator that updates an external SkillRepo from accumulated experience.<n>We show that SkillOS consistently outperforms memory-free and strong memory-based baselines in both effectiveness and efficiency.
  arXiv  Detail & Related papers  (2026-05-07T17:31:50Z)
• Skill1: Unified Evolution of Skill-Augmented Agents via Reinforcement Learning [32.51284576274437]
  A persistent skill library allows language model agents to reuse successful strategies across tasks.<n>Existing methods optimize these capabilities in isolation or with separate reward sources, resulting in partial and conflicting evolution.<n>We propose Skill1, a framework that trains a single policy to co-evolve skill selection, utilization, and distillation toward a shared task-outcome objective.
  arXiv  Detail & Related papers  (2026-05-07T12:33:30Z)
• Graph of Skills: Dependency-Aware Structural Retrieval for Massive Agent Skills [27.93279540484981]
  Graph of Skills (GoS) is an inference-time structural retrieval layer for large skill libraries.<n>GoS improves average reward by 43.6% over the vanilla full skill-loading baseline.<n>GoS consistently outperforms both vanilla skills loading and simple vector retrieval.
  arXiv  Detail & Related papers  (2026-04-07T02:09:11Z)
• SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization [61.37478254738943]
  Inference-time skill augmentation is fundamentally limited.<n>We introduce SKILL0, an in-context reinforcement learning framework designed for skill internalization.<n>SKILL0 achieves substantial improvements over the standard RL baseline.
  arXiv  Detail & Related papers  (2026-04-02T17:03:05Z)
• 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)
• STAR: Learning Diverse Robot Skill Abstractions through Rotation-Augmented Vector Quantization [87.77475595961154]
  We present textbfSkill textbfTraining with textbfAugmented textbfRotation (textbfSTAR), a framework that advances both skill learning and composition to complete complex behaviors.
  arXiv  Detail & Related papers  (2025-06-04T11:54:42Z)
• ELSIM: End-to-end learning of reusable skills through intrinsic motivation [0.0]
  We present a novel reinforcement learning architecture which hierarchically learns and represents self-generated skills in an end-to-end way. With this architecture, an agent focuses only on task-rewarded skills while keeping the learning process of skills bottom-up.
  arXiv  Detail & Related papers  (2020-06-23T11:20:46Z)

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.