OpenSkill: Open-World Self-Evolution for LLM Agents

• URL: http://arxiv.org/abs/2606.06741v1
• Date: Thu, 04 Jun 2026 21:55:48 GMT
• Title: OpenSkill: Open-World Self-Evolution for LLM Agents
• Abstract summary: We study open-world self-evolution, where an agent must build both its skills and its own verification signals from scratch.<n>We propose OpenSkill, a framework that bootstraps this loop.<n>OpenSkill attains the best automated pass rate while satisfying the no-supervision constraint.
• Score: 57.88680042818572
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Self-evolving agents requires adaptation after deployment, but existing approaches assume a usable learning loop, such as curated skills, successful trajectories, or verifier signals. Real open-world deployments may provide none of these, offering only a task prompt. In this work, we study open-world self-evolution, where an agent must build both its skills and its own verification signals from scratch, using open-world resources but no target-task supervision. We propose OpenSkill, a framework that bootstraps this loop: it acquires grounded knowledge and verification anchors from documentation, repositories, and the web, synthesizes them into transferable skills, and refines those skills against self-built virtual tasks grounded in the anchors rather than in target answers. The open world thus supplies both the knowledge to be learned and a supervision-independent practice environment, with target-task supervision reserved for final evaluation. Across three benchmarks and two target agents, OpenSkill attains the best automated pass rate while satisfying the no-supervision constraint. Analysis shows its skills transfer across models without model-specific adaptation, and its self-built verifier aligns with ground-truth outcomes despite never accessing them.

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)
• State2State: Environment-Derived Mid-Training for LLM Agents [67.08889234341996]
  We study an environment learning paradigm in which agents acquire interaction and manipulation capabilities solely through environment interaction.<n>We propose State2State, an environment-derived mid-training method that converts explored environment states into training objectives.<n> Experiments on ALFWorld and ScienceWorld show that State2State improves agent performance as a standalone environment-learning stage in most settings.
  arXiv  Detail & Related papers  (2026-08-05T15:02:41Z)
• Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills [53.15216425100295]
  We introduce Skill Self-Play (Skill-SP), a co-evolutionary framework comprising a proposer, a solver, and a skill controller.<n>The proposer generates challenging tasks conditioned on dynamically sampled skills; the solver explores candidate solutions to push its capability boundaries; and the skill controller collects execution feedback to update and expand the skill library.<n>This interactive co-evolution effectively bridges the gap between structured verification and open-ended exploration.
  arXiv  Detail & Related papers  (2026-07-24T17:59:22Z)
• OpenSkillEval: Automatically Auditing the Open Skill Ecosystem for LLM Agents [15.598856888948093]
  We present textscOpenSkillEval, an automatic evaluation framework for both skill-augmented agent systems and the skills themselves.<n>Instead of relying on static benchmarks, textscOpenSkillEval automatically constructs realistic task instances from evolving real-world artifacts.<n>Using more than 600 dynamically generated task instances and 30 open-source skills, we conduct a systematic evaluation of state-of-the-art models and agent frameworks.
  arXiv  Detail & Related papers  (2026-05-22T14:09:41Z)
• SkillX: Automatically Constructing Skill Knowledge Bases for Agents [27.56549031749266]
  SkillX is a fully automated framework for constructing a plug-and-play skill knowledge base.<n>It operates through a fully automated pipeline built on three synergistic innovations.<n> Experiments show that SkillKB consistently improves task success and execution efficiency when plugged into weaker base agents.
  arXiv  Detail & Related papers  (2026-04-06T16:09:33Z)
• SkillAttack: Automated Red Teaming of Agent Skills through Attack Path Refinement [66.44008181092832]
  LLM-based agent systems increasingly rely on agent skills sourced from open registries to extend their capabilities.<n>We introduce SkillAttack, a framework that verifies skill vulnerability exploitability through adversarial prompting.
  arXiv  Detail & Related papers  (2026-04-05T06:25:11Z)
• SoK: Agentic Skills -- Beyond Tool Use in LLM Agents [6.356997609995175]
  Agentic systems increasingly rely on reusable procedural capabilities, textita.k.a., agentic skills, to execute long-horizon reliably.<n>This paper maps the skill layer across the full lifecycle (discovery, practice, distillation, storage, composition, evaluation, and update)<n>We analyze the security and governance implications of skill-based agents, covering supply-chain risks, prompt injection via skill payloads, and trust-tiered execution.
  arXiv  Detail & Related papers  (2026-02-24T13:11:38Z)
• Agent Skills for Large Language Models: Architecture, Acquisition, Security, and the Path Forward [5.124116559484265]
  The transition from monolithic language models to modular, skill-equipped agents marks a defining shift in how large language models (LLMs) are deployed in practice.<n>Rather than encoding all procedural knowledge within model weights, agent skills enable dynamic capability extension without retraining.<n>This survey provides a comprehensive treatment of the agent skills landscape, as it has rapidly evolved during the last few months.
  arXiv  Detail & Related papers  (2026-02-12T21:33:25Z)
• InternBootcamp Technical Report: Boosting LLM Reasoning with Verifiable Task Scaling [71.37579508777843]
  Large language models (LLMs) have revolutionized artificial intelligence by enabling complex reasoning capabilities.<n>To address this gap, we present InternBootcamp, an open-source framework comprising 1000+ domain-diverse task environments.
  arXiv  Detail & Related papers  (2025-08-12T05:00:00Z)
• Human-Timescale Adaptation in an Open-Ended Task Space [56.55530165036327]
  We show that training an RL agent at scale leads to a general in-context learning algorithm that can adapt to open-ended novel embodied 3D problems as quickly as humans. Our results lay the foundation for increasingly general and adaptive RL agents that perform well across ever-larger open-ended domains.
  arXiv  Detail & Related papers  (2023-01-18T15:39:21Z)
• Choreographer: Learning and Adapting Skills in Imagination [60.09911483010824]
  We present Choreographer, a model-based agent that exploits its world model to learn and adapt skills in imagination. Our method decouples the exploration and skill learning processes, being able to discover skills in the latent state space of the model. Choreographer is able to learn skills both from offline data, and by collecting data simultaneously with an exploration policy.
  arXiv  Detail & Related papers  (2022-11-23T23:31:14Z)

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.