7 papers
Verifiable Process Rewards for Agentic Reasoning
Huining Yuan, Zelai Xu, Huaijie Wang +6
Reinforcement learning from verifiable rewards (RLVR) has improved the reasoning abilities of large language models (LLMs), but most existing approaches rely on sparse outcome-leve…
GAGPO: Generalized Advantage Grouped Policy Optimization
Siyuan Zhu, Chao Yu, Rongxin Yang +4
Reinforcement learning has become a powerful paradigm for post-training large language model agents, yet credit assignment in multi-turn environments remains a challenge. Agents of…
EvoMAS: Learning Execution-Time Workflows for Multi-Agent Systems
Chengdong Xu, Kaiqiang Ke, Ziheng Liu +4
Large language model (LLM)-based multi-agent systems have shown strong potential on complex tasks through agent specialization, tool use, and collaborative reasoning. However, most…
VS-Bench: Evaluating VLMs for Strategic Abilities in Multi-Agent Environments
Zelai Xu, Zhexuan Xu, Xiangmin Yi +7
Recent advancements in Vision Language Models (VLMs) have expanded their capabilities to interactive agent tasks, yet existing benchmarks remain limited to single-agent or text-onl…
RE-PO: Robust Enhanced Policy Optimization as a General Framework for LLM Alignment
Xiaoyang Cao, Zelai Xu, Mo Guang +4
Standard human preference-based alignment methods, such as Reinforcement Learning from Human Feedback (RLHF), are a cornerstone for aligning large language models (LLMs) with human…
MARSHAL: Incentivizing Multi-Agent Reasoning via Self-Play with Strategic LLMs
Huining Yuan, Zelai Xu, Zheyue Tan +10
Developing Large Language Models (LLMs) to cooperate and compete effectively within multi-agent systems (MASs) is a critical step towards more advanced intelligence. While reinforc…