{"slug": "envharness-awakening-static-worlds-for-agent-learning", "title": "EnvHarness: Awakening Static Worlds for Agent Learning", "summary": "Researchers introduced EnvHarness, a programmable layer of plug-in components that reshapes static environments for LLM agent learning without modifying underlying logic, and EnvRigger, which synthesizes these components by observing a target policy's trajectories. Across five benchmarks in four domains, EnvHarness outperformed original environments and domain-specific pipelines, achieving up to a 9.0-point improvement on held-out instances with 9.8% fewer execution steps, and provided a superior optimization signal for reinforcement learning.", "body_md": "# Computer Science > Artificial Intelligence\n\n[Submitted on 20 Aug 2026]\n\n# Title:EnvHarness: Awakening Static Worlds for Agent Learning\n\n[View PDF](/pdf/2608.19880)\n\nAbstract:LLM agents learn by interacting with environments, yet these environments are hand-built and static: blind to an agent's weaknesses, and quickly left behind as it improves. While recent environment generation methods attempt to address this, they require domain-specific pipelines, rely on expensive or unreliable verifiers, and still produce static environments. To alleviate the engineering burden of rebuilding environments from scratch, we propose Environment Harness (EnvHarness), a programmable layer of plug-in components that wraps a static environment to reshape its behavior without modifying the underlying logic. Operating through standard interfaces, EnvHarness applies across diverse domains while ensuring every reshaped environment retains its original verifier. To automate this process, we introduce EnvRigger, which treats the target policy as a black box, observing its execution trajectories to synthesize EnvHarness components targeting diagnosed flaws, and validating them via fresh rollouts. Across five benchmarks in four domains, EnvHarness outperforms both original environments and domain-specific environment generation pipelines, achieving up to a 9.0-point improvement on held-out instances with 9.8% fewer execution steps. Furthermore, EnvHarness provides a superior optimization signal for reinforcement learning, enabling continuous, targeted co-evolution of the policy and its environment.\n\n### Current browse context:\n\ncs.AI\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/envharness-awakening-static-worlds-for-agent-learning", "canonical_source": "https://arxiv.org/abs/2608.19880", "published_at": "2026-08-22 06:08:19+00:00", "updated_at": "2026-08-22 06:43:53.405863+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "ai-research", "ai-agents"], "entities": ["EnvHarness", "EnvRigger"], "alternates": {"html": "https://wpnews.pro/news/envharness-awakening-static-worlds-for-agent-learning", "markdown": "https://wpnews.pro/news/envharness-awakening-static-worlds-for-agent-learning.md", "text": "https://wpnews.pro/news/envharness-awakening-static-worlds-for-agent-learning.txt", "jsonld": "https://wpnews.pro/news/envharness-awakening-static-worlds-for-agent-learning.jsonld"}}