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 authors propose a method called Environment Harness (EnvHarness) to address the issue of static environments in learning agents. This system allows for reshaping behavior without modifying the underlying logic, operating through standard interfaces. It introduces EnvRigger to automate the process by diagnosing flaws and synthesizing components. The study shows that EnvHarness outperforms original environments and domain-specific pipelines across five benchmarks in four domains, achieving up to a 9.0-point improvement on held-out instances with fewer execution steps.

What the Document Says

• The proposed method is called Environment Harness (EnvHarness).
• It wraps a static environment to reshape its behavior without modifying the underlying logic.
• Operates through standard interfaces, applicable across diverse domains.
• Ensures every reshaped environment retains its original verifier.
• Introduces EnvRigger to automate the process.
• Treats the target policy as a black box.
• Observes its execution trajectories to synthesize EnvHarness components.
• Validates them via fresh rollouts.
• Shows improved performance in five benchmarks across four domains.
• Achieves up to a 9.0-point improvement on held-out instances.
• Uses fewer execution steps (9.8%).
• Outperforms both original environments and domain-specific environment generation pipelines.

Key Concepts Mentioned

• Environment Harness
• EnvRigger
• static environments
• learning agents
• reinforcement learning
• benchmarks
• execution steps

Claims Made by the Document

• EnvHarness improves performance over original environments and domain-specific pipelines.
• EnvHarness reduces the number of execution steps.

Evidence Used

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

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

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.