Hugging Face Trending Papers July 5, 2026

Learning to Control LLM Agent Harnesses with Offline Reinforcement Learning

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Large language model (LLM) agents are usually improved by changing prompts, models, or hand-written workflows, while the execution harness around the model is treated as fixed infrastructure. We argue that this harness is itself a learnable control layer.

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arXiv AI

Jul 8

Learning to Control LLM Agent Harnesses with Offline Reinforcement Learning

arXiv:2607. 05458v1 Announce Type: cross Abstract: Large language model (LLM) agents are usually improved by changing prompts, models, or hand-written workflows, while the execution harness around the model is treated as fixed infrastructure.

By Haiwen Yi, Xinyuan Song

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Aug 7

EvoHarness-RL: Learning Self-Evolving Runtime Harness for Long-Horizon LLM Agents

arXiv:2608. 05446v1 Announce Type: new Abstract: Long-horizon LLM agents increasingly rely on external execution support to maintain state, track progress, invoke tools, verify outcomes, and reuse experience across interactions.

By Xuying Ning, Dongqi Fu, Tianxin Wei, Hanqing Zeng, Yuanchen Bei, Bingxuan Li, Zihao Li, Qifan Wang, Xiang Shen, Yifan Wu, Jiayi Liu, Hong Li, Yinglong Xia, Xiangjun Fan, Hanghang Tong, Jingrui He

llms agents reinforcement-learning fine-tuning

arXiv AI

Jun 30

SCRIBE: Structured Mid-Level Supervision for Tool-Using Language Models

arXiv:2601. 03555v3 Announce Type: replace Abstract: Training reliable tool-augmented agents remains a significant challenge, largely due to the difficulty of credit assignment in multi-step reasoning.

By Yuxuan Jiang, Francis Ferraro

llms agents reinforcement-learning benchmarks

arXiv AI

Jul 29

A Control System, a Dataset, and a Recipe for Making Frozen LLM Agents Learn a Domain

arXiv:2607. 25415v1 Announce Type: new Abstract: Production LLM agents are increasingly assembled from a frozen model wrapped in a harness: a prompt template, a tool set, a memory/retrieval layer, a planning strategy, and a verification policy.

By Debjyoti Paul

llms agents reinforcement-learning benchmarks

arXiv AI

Aug 12

Efficient Reinforcement Learning for Long-Horizon Tool-Use Agentic Tasks

arXiv:2608. 10357v1 Announce Type: cross Abstract: Long-horizon tool-using agents must reason over user goals, domain policies, tool calls, simulator state, and delayed verifiable rewards.

By Zelei Cheng, Amritansh Mishra, Sambit Sahu, William Campbell

agents reinforcement-learning benchmarks

arXiv AI

Aug 7

EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning

arXiv:2608. 06197v1 Announce Type: new Abstract: Training large language model agents for long-horizon tool use typically relies on interactions with real or synthesized executable environments, whose construction and verification are costly, or on external simulators that are difficult to ground.

By Zishan Xu, Zhiyuan Yao, Yuxin Chen, Yifu Guo, Zhengxi Lu, Yuquan Lu, Jinyang Huang, Yan Xu, Yasheng Wang, Weinan Zhang, Xingshan Zeng, Weiwen Liu

llms agents reinforcement-learning

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Learning to Control LLM Agent Harnesses with Offline Reinforcement Learning

EvoHarness-RL: Learning Self-Evolving Runtime Harness for Long-Horizon LLM Agents

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EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning