EnvHarness: Awakening Static Worlds for Agent Learning

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Chengsong Huang, Zifeng Wang, Rujun Han, Jun Yan, Yanfei Chen, Zoey CuiZhu, Ke Jiang, Peng Xia, Han Yu, Yufan Zhuang, Yifei Ming, Jiaqi Pan, Bhavana Dalvi Mishra, Jiaxin Huang, Burak Gokturk, Tomas Pfister, Chen-Yu Lee
• Venue: arXiv: cs.AI
• Published: 2026-08-20
• Collected: 2026-08-21
• URL: http://arxiv.org/abs/2608.19880v1
• PDF: https://arxiv.org/pdf/2608.19880v1
• DOI: UNKNOWN
• Registry group: academic_apis_and_preprints
• Evidence role: canonical_academic_index
• Language: en
• Country / region: UNKNOWN

Plain-Language Brief

The paper proposes a new system called Environment Harness (EnvHarness) to improve static environments used in learning by AI agents. The system can adapt the behavior of these environments without modifying their underlying logic, making them more responsive to an agent's weaknesses. The system, EnvRigger, synthesizes EnvHarness components based on the target policy's execution trajectories, validates them through fresh rollouts, and performs better than original environments and domain-specific environment generation pipelines in various benchmarks.

What the Document Says

• The paper discusses a system called Environment Harness (EnvHarness).
• EnvHarness is designed to wrap static environments to reshape their behavior without modifying the underlying logic.
• The EnvRigger component treats the target policy as a black box and synthesizes EnvHarness components based on its execution trajectories.
• The validity of the synthesized components is verified through fresh rollouts.

Key Concepts Mentioned

• AI agents
• learning environments
• EnvHarness
• EnvRigger
• static environments
• execution trajectories

Claims Made by the Document

• EnvHarness outperforms both original environments and domain-specific environment generation pipelines.
• EnvHarness provides a superior optimization signal for reinforcement learning.

Evidence Used

• achieving up to a 9.0-point improvement on held-out instances with 9.8% fewer execution steps

Methods / Approach

• Applying standard interfaces
• Synthesizing EnvHarness components
• Verifying the validity of synthesized components through fresh rollouts

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

The paper does not specify any limitations mentioned by the authors.

Open Questions

• What are the specific details of the five benchmarks in four domains?
• How exactly is the 'optimization signal' improved for reinforcement learning?

Notes for Future Q&A

May include interpretation. Labeled where it does.

• The paper mentions five benchmarks but does not specify what they are or in which domains they are found.
• The phrase 'superior optimization signal' implies an improvement, but the specifics of how this is achieved are not detailed.

Raw Abstract / Excerpt

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.