arXiv AI June 30, 2026 By Yihang Yao, Zhepeng Cen, Haohong Lin, Shiqi Liu, Zuxin Liu, Jiacheng Zhu, Zhang-Wei Hong, Laixi Shi, Ding Zhao

Pushing Forward Pareto Frontiers of Proactive Agents with Behavioral Agentic Optimization

Read the original on arXiv AI →

arXiv:2602. 11351v2 Announce Type: replace Abstract: Proactive large language model (LLM) agents aim to actively plan, query, and interact over multiple turns, enabling efficient task completion beyond passive instruction following and making them essential for real-world, user-centric applications.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 23

In-the-Flow Agentic System Optimization for Effective Planning and Tool Use

arXiv:2510. 05592v2 Announce Type: replace Abstract: Outcome-driven reinforcement learning has advanced reasoning in large language models (LLMs), but prevailing tool-augmented approaches train a single, monolithic policy that interleaves thoughts and tool calls under full context; this scales poorly with long horizons and diverse tools and generalizes weakly to new scenarios.

By Zhuofeng Li, Haoxiang Zhang, Seungju Han, Sheng Liu, Jianwen Xie, Yu Zhang, Yejin Choi, James Zou, Pan Lu

llms agents reinforcement-learning benchmarks

arXiv AI

Jun 2

On Effectiveness and Efficiency of Agentic Tool-calling and RL Training

arXiv:2606. 00135v1 Announce Type: cross Abstract: Tool-calling is a central component of modern large language model (LLM) agents, equipping them with skills beyond their parametric knowledge.

By Tong Liu, Cheng Qian, Matej Cief, Yuan He, Daniele Dan, Nikolaos Aletras, Gabriella Kazai

llms agents reinforcement-learning benchmarks

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 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 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

Jun 18

InfoPO: Information-Driven Policy Optimization for User-Centric Agents

arXiv:2603. 00656v2 Announce Type: replace Abstract: Real-world user requests to LLM agents are often underspecified.

By Fanqi Kong, Jiayi Zhang, Mingyi Deng, Chenglin Wu, Yuyu Luo, Bang Liu

llms agents reinforcement-learning

Related stories

In-the-Flow Agentic System Optimization for Effective Planning and Tool Use

On Effectiveness and Efficiency of Agentic Tool-calling and RL Training

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

TAPO: Transition-Aware Policy Optimization for LLM Agents

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

InfoPO: Information-Driven Policy Optimization for User-Centric Agents