5 papers
Next-Generation Agentic Reinforcement Learning Systems Enable Self-Evolving Agents
Ran Yan, Wei Fu, Jiale Li +21
LLM agents are rapidly being deployed in production, including coding assistants, customer-support chatbots, and scientific research assistants, yet they remain fundamentally stati…
Building Multi-Task Agentic LLMs via Two-Phase Distillation
Huaijie Wang, Shusheng Xu, Yi Wu +1
A key step toward artificial general intelligence is to train models that can perform multiple tasks. In this paper, we study how to build such models by first training separate RL…
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…
Offline Reinforcement Learning for LLM Multi-Step Reasoning
Huaijie Wang, Shibo Hao, Hanze Dong +4
Improving the multi-step reasoning ability of large language models (LLMs) with offline reinforcement learning (RL) is essential for quickly adapting them to complex tasks. While D…
LAGOON: Language-Guided Motion Control
Shusheng Xu, Huaijie Wang, Jiaxuan Gao +3
We aim to control a robot to physically behave in the real world following any high-level language command like "cartwheel" or "kick". Although human motion datasets exist, this ta…