arXiv AI June 2, 2026 By Junwei Liao, Muning Wen, Jun Wang, Weinan Zhang

MARFT: Multi-Agent Reinforcement Fine-Tuning

arXiv:2504. 16129v5 Announce Type: replace-cross Abstract: Large Language Model (LLM)-based Multi-Agent Systems (LaMAS) have demonstrated strong capabilities on complex agentic tasks requiring multifaceted reasoning and collaboration, from high-quality presentation generation to scientific research.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jul 7

A Technical Survey of Reinforcement Learning Techniques for Large Language Models

arXiv:2507. 04136v2 Announce Type: replace Abstract: This survey offers a comprehensive foundation on the integration of RL with language models, highlighting prominent algorithms such as Proximal Policy Optimization (PPO), Q-Learning, and Actor-Critic methods.

By Saksham Sahai Srivastava, Vaneet Aggarwal

llms reinforcement-learning benchmarks safety

arXiv AI

2d ago

Meta-Multi-Agent Reinforcement Learning for Fast Adaptation of Interactive Policies with Applications to Autonomous Driving

The paper introduces a meta-multi-agent reinforcement learning (meta‑MARL) framework that enables rapid adaptation of interactive policies in multi‑agent systems. By modeling multi‑agent reinforcement learning problems as Markov games and defining a new concept called meta‑NE, the authors establish conditions linking meta‑NE to stationary points of a gradient‑play meta‑MARL algorithm. Experiments on autonomous‑driving tasks show that this approach adapts faster than pretrained MARL baselines, demonstrating its effectiveness.

By Huiwen Yan, Kyriakos G. Vamvoudakis, Mushuang Liu

agents reinforcement-learning robotics

arXiv Machine Learning

Jun 25

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems

arXiv:2606. 24937v1 Announce Type: cross Abstract: The Hitchhiker's Guide to Agentic AI is a comprehensive practitioner's reference for building autonomous AI systems.

By Haggai Roitman

llms rag agents reinforcement-learning fine-tuning efficiency safety

arXiv AI

Aug 14

AHD Agent: Agentic Reinforcement Learning for Automatic Heuristic Design

arXiv:2605. 08756v2 Announce Type: replace Abstract: Automatic heuristic design (AHD) has emerged as a promising paradigm for solving NP-hard combinatorial optimization problems (COPs).

By Haoze Lv, Ning Lu, Ziang Zhou, Yew-Soon Ong, Shengcai Liu

llms agents 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 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

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Related stories

A Technical Survey of Reinforcement Learning Techniques for Large Language Models

Meta-Multi-Agent Reinforcement Learning for Fast Adaptation of Interactive Policies with Applications to Autonomous Driving

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems

AHD Agent: Agentic Reinforcement Learning for Automatic Heuristic Design

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

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