Break It Down, Pass It On: Cross-Task Skill Transfer in LLM Agents

• URL: http://arxiv.org/abs/2608.20274v1
• Date: Thu, 20 Aug 2026 17:12:08 GMT
• Title: Break It Down, Pass It On: Cross-Task Skill Transfer in LLM Agents
• Abstract summary: 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.
• Score: 17.291924088593408
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language model (LLM) agents can induce skills from completed tasks and reuse them later to grow more capable with experience. In practice, induced skills may transfer unreliably and can even harm the agent that retrieves them. When agent-induced skills transfer reliably across tasks remains an open question. We conduct a comprehensive and controlled study of how the way skills are induced shapes their transfer across tasks. Specifically, we compare task-level with subtask-level skill induction and text with code skill formats, the two axes along which existing methods differ. Task-level skills mostly reduce the agent's performance below its no-memory baseline while subtask-level skills raise it above on average, and text skills transfer better than code skills. To further understand our findings, we examine two complementary properties of the induced skills: specificity, which measures how closely a skill matches real tasks, and abstractness, which measures how evenly its relevance spreads across tasks. Neither property alone predicts task success, but their combined effect does, which we propose as a skill utility score. The score correlates consistently with task success when skills are transferred, and subtask-level and text skills score higher. Computing skill utility only needs the skills and task descriptions but not any task execution, so our score serves as a practical diagnostic of a skill memory before any new task runs.

Related papers

• SkillEval: Decomposing Agent Skill Quality into Interpretable Signals [27.757569384137767]
  General properties of the textttSKILL.md document play an important role in skill quality.<n>SkillEval learns an interpretable direction for each quality property from controlled positive--negative skill pairs.<n>SkillEval scores closely reflect downstream task performance, providing an early indication of whether a skill is likely to help an agent complete a task.
  arXiv  Detail & Related papers  (2026-08-07T07:25:17Z)
• SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution [39.74364990208475]
  Large language model agents often encounter related yet distinct tasks that share reusable solution patterns.<n>We introduce SkillRise, a unified reinforcement learning framework for learning skills across tasks.
  arXiv  Detail & Related papers  (2026-07-29T11:26:33Z)
• Task Decomposition-Guided Reranking for Adaptive Agent Skill Retrieval [16.270741233471107]
  We propose SkillReranker, an inference-time reranking framework for adaptive skill selection.<n>We show that SkillReranker effectively improves task performance, reduces environment interaction steps, and lowers token consumption.
  arXiv  Detail & Related papers  (2026-07-07T13:49:08Z)
• 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)
• Skill Coverage: A Test Adequacy Metric for Agent Skills [17.60316723069748]
  Agent skills encode reusable procedural knowledge that guides large language model agents across tasks and execution contexts.<n>We introduce skill coverage, a test adequacy metric that treats the skill artifact as the object under test.<n>Applying skill coverage to SkillsBench reveals that existing benchmark executions cover only 39.90 to 43.98% of skill behavior constraints.
  arXiv  Detail & Related papers  (2026-06-09T10:16:05Z)
• 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)
• SkillGraph: Skill-Augmented Reinforcement Learning for Agents via Evolving Skill Graphs [70.1970574147839]
  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.
  arXiv  Detail & Related papers  (2026-05-12T12:21:49Z)
• 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)
• Hierarchical Skills for Efficient Exploration [70.62309286348057]
  In reinforcement learning, pre-trained low-level skills have the potential to greatly facilitate exploration. Prior knowledge of the downstream task is required to strike the right balance between generality (fine-grained control) and specificity (faster learning) in skill design. We propose a hierarchical skill learning framework that acquires skills of varying complexity in an unsupervised manner.
  arXiv  Detail & Related papers  (2021-10-20T22:29:32Z)
• Discovering Generalizable Skills via Automated Generation of Diverse Tasks [82.16392072211337]
  We propose a method to discover generalizable skills via automated generation of a diverse set of tasks. As opposed to prior work on unsupervised discovery of skills, our method pairs each skill with a unique task produced by a trainable task generator. A task discriminator defined on the robot behaviors in the generated tasks is jointly trained to estimate the evidence lower bound of the diversity objective. The learned skills can then be composed in a hierarchical reinforcement learning algorithm to solve unseen target tasks.
  arXiv  Detail & Related papers  (2021-06-26T03:41:51Z)
• 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.