arXiv AI

Distilling LLM Reasoning into an Interpretable Policy Tree for Human-AI Collaboration

arXiv:2606. 08596v1 Announce Type: new Abstract: Constructing efficient and reliable policies to assist humans is indispensable for human-AI collaboration.

arXiv AI

Jun 26

Joint Learning of Experiential Rules and Policies for Large Language Model Agents

arXiv:2606. 27136v1 Announce Type: new Abstract: For LLM agents in multi-step interactive environments, a key challenge is to make effective use of accumulated interaction experience.

By Shicheng Ye, Chao Yu

llms agents

arXiv AI

Jun 4

ChatSOP: An SOP-Guided MCTS Planning Framework for Controllable LLM Dialogue Agents

arXiv:2407. 03884v4 Announce Type: replace-cross Abstract: Dialogue agents powered by Large Language Models (LLMs) show superior performance in various tasks.

By Zhigen Li, Jianxiang Peng, Yanmeng Wang, Yong Cao, Tianhao Shen, Minghui Zhang, Linxi Su, Shang Wu, Yihang Wu, Yuqian Wang, Ye Wang, Wei Hu, Jianfeng Li, Shaojun Wang, Jing Xiao, Deyi Xiong

llms agents fine-tuning

arXiv AI

Jul 7

Interactive Learning for LLM Reasoning

arXiv:2509. 26306v5 Announce Type: replace Abstract: Existing multi-agent learning approaches have developed interactive training environments to explicitly promote collaboration among multiple Large Language Models (LLMs), thereby constructing stronger multi-agent systems (MAS).

By Hehai Lin, Shilei Cao, Sudong Wang, Haotian Wu, Minzhi Li, Linyi Yang, Juepeng Zheng, Chengwei Qin

llms agents nlp benchmarks

arXiv Machine Learning

Jul 31

TAPO: Transition-Aware Policy Optimization for LLM Agents

arXiv:2607. 27973v1 Announce Type: new Abstract: Recently, Reinforcement Learning (RL) has emerged as a crucial paradigm for the post-training of Large Language Model (LLM) agents.

By Cong Li, Peixi Peng, Yisen Zhao, Xinyu Hu, Shudong Liu, Zhan Su, Zhuojian Li

llms agents reinforcement-learning

arXiv AI

Jun 10

TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning

arXiv:2606. 11119v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is a promising approach for enhancing reasoning and agentic behavior in large language models.

By Heming Zou, Qi Wang, Yun Qu, Yuhang Jiang, Lizhou Cai, Yixiu Mao, Ru Peng, Xin Xu, Weijie Liu, Kai Yang, Saiyong Yang, Xiangyang Ji

llms agents reinforcement-learning benchmarks

arXiv AI

Aug 11

Branch2Skill: Efficient Skill Evolution Through Reasoning Trees

arXiv:2608. 08677v1 Announce Type: new Abstract: Skill evolution improves agent skills through feedback over time, with failed trajectories often providing informative signals by revealing incomplete or misleading behaviors.

By Yanwei Ren, Haotian Zhang, Likang Xiao, Jiaxing Huang, Jiayan Qiu, Baosheng Yu, Quan Chen, Liu Liu

llms agents benchmarks

arXiv Machine Learning

Jun 9

CATPO: Critique-Augmented Tree Policy Optimization

arXiv:2606. 08346v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a dominant paradigm for improving the reasoning capabilities of large language models (LLMs).

By Ayush Singh, Umang Goyal, Ankur Dahiya

llms reinforcement-learning benchmarks

arXiv Machine Learning

Jun 3

Multi$^2$: Hierarchical Multi-Agent Decision-Making with LLM-Based Agents in Interactive Environments

arXiv:2606. 03698v1 Announce Type: new Abstract: A central goal of large language model (LLM) research is to build agentic systems that can plan, act, and adapt through sustained interaction with dynamic environments.

By Sangeun Park, Minhae Kwon

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 24

ARCO: Adaptive Rubrics with Co-Evolution for Multi-Step LLM-Based Agents

arXiv:2606. 21262v2 Announce Type: replace Abstract: Reinforcement learning for multi-step LLM agents often relies on scalar rewards that indicate success but cannot explain why a trajectory is good or bad.

By Zihang Tian, Jingsen Zhang, Rui Li, Xiaohe Bo, Yuanzi Li, Xu Chen

llms agents reinforcement-learning safety

arXiv AI

Jun 2

Policy and World Modeling Co-Training for Language Agents

arXiv:2606. 02388v1 Announce Type: cross Abstract: Reinforcement learning (RL) improves large language model (LLM) agents by teaching them which actions lead to high rewards, but provides little supervision on what those actions do to the environment.

By Ning Lu, Baijiong Lin, Shengcai Liu, Jiahao Wu, Haoze Lv, Yanbin Wei, Lingting Zhu, Shengju Qian, Xin Wang, Ying-Cong Chen, Qi Wang, Ke Tang

llms agents reinforcement-learning benchmarks

arXiv AI

Jun 16

OpenClaw-Skill: Collective Skill Tree Search for Agentic Large Language Models

arXiv:2606. 16774v1 Announce Type: new Abstract: Equipping Large Language Model (LLM) agents with effective skills is crucial for solving complex tasks in real-world systems like OpenClaw.

By Tianyi Lin, Chuanyu Sun, Jingyi Zhang, Changxu Wei, Huanjin Yao, Shunyu Liu, Xikun Zhang, Liu Liu, Jiaxing Huang

llms agents reinforcement-learning benchmarks

Hugging Face Trending Papers

Jul 9

GATS: Graph-Augmented Tree Search with Layered World Models for Efficient Agent Planning

Large Language Model (LLM) agents have shown promise in multi-step planning tasks, but existing approaches like LATS (Language Agent Tree Search) and ReAct rely heavily on LLM inference during planning, leading to high computational costs and stochastic behavior. We present \textbf{GATS} (Graph-Augmented Tree Search), a planning framework that combines systematic UCB1-based tree search with a layered world model to eliminate LLM calls during inference while achieving superior planning performance.

llms agents

Related stories