SkillForge: Evolving Verifiable Skills for Reinforcement Learning Agents

• URL: http://arxiv.org/abs/2608.24747v1
• Date: Tue, 25 Aug 2026 15:52:10 GMT
• Title: SkillForge: Evolving Verifiable Skills for Reinforcement Learning Agents
• Abstract summary: Large language model (LLM) agents are trained with reinforcement learning (RL) for complex decision-making tasks.<n>Recent skill-based approaches, such as SkillRL, attempt to address this issue by extracting skills from raw trajectories.<n>We propose SkillForge, a framework for continuous skill evolution that enables skills to be verified and refined through environment interaction.
• Score: 17.565432600589414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language model (LLM) agents are trained with reinforcement learning (RL) for complex decision-making tasks. However, most RL-trained agents remain episodic and cannot accumulate reusable knowledge across episodes. Recent skill-based approaches, such as SkillRL, attempt to address this issue by extracting skills from raw trajectories, but treat the skill bank as an append-only repository without verifying whether stored skills remain effective. In this paper, we propose SkillForge, a framework for continuous skill evolution that enables skills to be verified and refined through environment interaction. By making skill usage explicit during agent interaction, RL can directly optimize both environment actions and skill invocation decisions. SkillForge further introduces evidence-based skill verification and multi-pathway skill induction, allowing the skill bank to continuously grow while maintaining its quality. Extensive experiments on ALFWorld, WebShop, and AppWorld show that SkillForge consistently outperforms SkillRL, demonstrating the effectiveness of continuously verified skills in training stronger LLM agents.

Related papers

• Co-Evolving Skill Generation and Policy Optimization [35.41582114275514]
  Existing methods typically use strong language models to analyze trajectories, generate skills, and update a retrievable skill bank during online training.<n>We propose an online reinforcement learning framework for pre-storage skill validation.
  arXiv  Detail & Related papers  (2026-06-07T17:55:55Z)
• 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)
• SKILLC: Learning Autonomous Skill Internalization in LLM Agents via Contrastive Credit Assignment [4.336665585098371]
  We propose SkillC, a framework based on Contrastive Skill Credit Assignment (CSCA) that converts this contrast into a direct learning signal for internalization.<n>textscSkillC samples paired skill-injected and skill-free rollouts for tasks from active skill types within the same policy update.<n>A smoothed validation-level signal further drives an adaptive curriculum over attribution strength, rollout allocation, and monotonic active-set pruning.
  arXiv  Detail & Related papers  (2026-05-27T03:21:19Z)
• 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)
• SkillMaster: Toward Autonomous Skill Mastery in LLM Agents [27.651128308229378]
  We propose SkillMaster, a training framework that teaches agents to create new skills, refine existing skills, and select accumulated skills during task solving.<n>First, we train agents through trajectory-informed skill review, teaching agents to propose, update, or retain skills based on evidence from completed episodes.<n>Second, each candidate skill edit is designed to be evaluated by its counterfactual utility on related probe tasks, providing a direct learning signal for training skill-editing decisions.<n>Third, we introduce DualAdv-GRPO, which separately estimates advantages for task-solving actions and skill-editing decisions, stabilizing joint training across task solving and
  arXiv  Detail & Related papers  (2026-05-09T05:03:00Z)
• 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)
• From Context to Skills: Can Language Models Learn from Context Skillfully? [56.5857437218136]
  Ctx2Skill is a self-evolving framework that autonomously discovers, refines, and selects context-specific skills.<n>It can be plugged into any language model to obtain better context learning capability.
  arXiv  Detail & Related papers  (2026-04-30T09:53:15Z)
• Co-Evolving LLM Decision and Skill Bank Agents for Long-Horizon Tasks [83.4942519570046]
  Large Language Models (LLMs) offer a promising alternative as game playing agents, but they often struggle with consistent long horizon decision making.<n>We present COSPLAY, a co evolution framework in which an LLM decision agent retrieves skills from a learnable skill bank to guide action taking.<n>Our framework improves both the decision agent to learn better skill retrieval and action generation, while the skill bank agent continually extracts, refines, and updates skills together with their contracts.
  arXiv  Detail & Related papers  (2026-04-22T18:17:17Z)
• EvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification [85.3692584167951]
  Anthropic proposes the concept of skills for LLM agents to tackle multi-step professional tasks.<n>A tool is a single, self-contained function, whereas a skill is a structured bundle of interdependent multi-file artifacts.<n>EvoSkills is a self-evolving skills framework that enables agents to autonomously construct complex, multi-file skill packages.
  arXiv  Detail & Related papers  (2026-04-02T06:43:20Z)
• Dynamic Dual-Granularity Skill Bank for Agentic RL [34.161117844675324]
  D2Skill is a dynamic dual-granularity skill bank for agentic reinforcement learning.<n>It organizes reusable experience into task skills for high-level guidance and step skills for fine-grained decision support and error correction.
  arXiv  Detail & Related papers  (2026-03-30T17:32:11Z)

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.