1 citations · 2 across the 5 of their papers we have counts for
6 papers
The Weight of Teamwork: Does Communication Structure Help LLM Scientists Search for Scientific Laws?
Jianing Shi, Zaibin Zhang, Shijun Lei +1
Team performance is often reported as one delivered score, even though that score is produced in two stages: a system generates candidate answers and a selection rule returns one o…
MA-VLA: Multi-Arm Vision-Language-Action Model for Collaboration and Compositional Generalization
Zaibin Zhang, Junlan Xiao, Zhongbo Zhang +11
Multi-arm collaboration is becoming a core capability in embodied manipulation. Recent vision-language-action (VLA) models integrate perception, language, and control, but most rep…
CoEnv: Driving Embodied Multi-Agent Collaboration via Compositional Environment
Li Kang, Yutao Fan, Rui Li +11
Multi-agent embodied systems hold promise for complex collaborative manipulation, yet face critical challenges in spatial coordination, temporal reasoning, and shared workspace awa…
CoMAS: Co-Evolving Multi-Agent Systems via Interaction Rewards
Xiangyuan Xue, Yifan Zhou, Guibin Zhang +7
Self-evolution is a central research topic in enabling large language model (LLM)-based agents to continually improve their capabilities after pretraining. Recent research has witn…
Scaling Behaviors of LLM Reinforcement Learning Post-Training: An Empirical Study in Mathematical Reasoning
Zelin Tan, Hejia Geng, Xiaohang Yu +14
While scaling laws for large language models (LLMs) during pre-training have been extensively studied, their behavior under reinforcement learning (RL) post-training remains largel…
The Landscape of Agentic Reinforcement Learning for LLMs: A Survey
Guibin Zhang, Hejia Geng, Xiaohang Yu +22
The emergence of agentic reinforcement learning (Agentic RL) marks a paradigm shift from conventional reinforcement learning applied to large language models (LLM RL), reframing LL…