홈 › Papers

Collaborative Multi-Agent Test-Time Reinforcement Learning for Reasoning

2026-01-14 · Zhiyuan Hu, Yunhai Hu, Juncheng Liu, Shuyue Stella Li, Yucheng Wang, Zhen Xu, See-Kiong Ng, Anh Tuan Luu, Xinxing Xu, Bryan Hooi, Cynthia Breazeal, Hae Won Park arxiv

Multi-agent systems have evolved into practical LLM-driven collaborators for many applications, gaining robustness from diversity and cross-checking. However, multi-agent RL (MARL) training is resource-intensive and unstable: co-adapting teammates induce non-stationarity, and rewards are often sparse and high-variance. Therefore, we introduce \textbf{Multi-Agent Test-Time Reinforcement Learning (MATTRL)}, a framework that injects structured textual experience into multi-agent deliberation at inference time. MATTRL forms a multi-expert team of specialists for multi-turn discussions, retrieves and integrates test-time experiences, and reaches consensus for final decision-making. We also study credit assignment for constructing a turn-level experience pool, then reinjecting it into the dialogue. Across challenging benchmarks in medicine, math, and education, MATTRL improves accuracy by an average of 3.67\% over a multi-agent baseline, and by 8.67\% over comparable single-agent baselines. Ablation studies examine different credit-assignment schemes and provide a detailed comparison of how they affect training outcomes. MATTRL offers a stable, effective and efficient path to distribution-shift-robust multi-agent reasoning without tuning.

📄 PDF Abstract BibTeX arXiv:2601.09667

Code (0)

등록된 구현이 없습니다.

Tasks

Reinforcement Learning

K-nearest Multi-agent Deep Reinforcement Learning for Collaborative Tasks with a Variable Number of Agents

2022-01-18 · Hamed Khorasgani, HaiYan Wang, Hsiu-Khuern Tang, Chetan Gupta

Traditionally, the performance of multi-agent deep reinforcement learning algorithms are demonstrated and validated in gaming environments where we often have a fixed number of agents. In many industrial applications, th…

Deep Reinforcement Learning Management reinforcement-learning Reinforcement Learning+1

Toward Collaborative Reinforcement Learning Agents that Communicate Through Text-Based Natural Language

2021-07-20 · Kevin Eloff, Herman A. Engelbrecht

Communication between agents in collaborative multi-agent settings is in general implicit or a direct data stream. This paper considers text-based natural language as a novel form of communication between multiple agents…

Navigate reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Heterogeneous Agent Collaborative Reinforcement Learning

2026-03-03 · Zhixia Zhang, Zixuan Huang, Gongxun Li, Huaiyang Wang 외 arxiv

We introduce Heterogeneous Agent Collaborative Reinforcement Learning (HACRL), a new Reinforcement Learning from Verifiable Reward (RLVR) problem that addresses the inefficiencies of isolated multi-agent on-policy optimi…

Multi-agent Reinforcement Learning

Reinforcement Learning-Augmented LLM Agents for Collaborative Decision Making and Performance Optimization

2025-12-31 · Dong Qiu, Duo Xu, Limengxi Yue arxiv

Large Language Models (LLMs) perform well in language tasks but often lack collaborative awareness and struggle to optimize global performance in multi-agent settings. We present a reinforcement learning-augmented LLM ag…

Reinforcement Learning Decision Making

COLLABORATIVE MULTIAGENT REINFORCEMENT LEARNING IN HOMOGENEOUS SWARMS

2018-09-27 · Arbaaz Khan, Clark Zhang, Vijay Kumar, Alejandro Ribeiro

A deep reinforcement learning solution is developed for a collaborative multiagent system. Individual agents choose actions in response to the state of the environment, their own state, and possibly partial information a…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)