1 citations · 1 across the 3 of their papers we have counts for
5 papers
UniSkill: Learning Actor-Aligned Skill Proposals for an Evolving Policy
Yifei Lu, Cheng Liu, Dianzhi Yu +4
Large language model agents can improve across tasks by retaining reusable skills distilled from prior interactions. Recent work jointly optimizes task execution and skill extracti…
CoEvoke: Evoking Multi-Agent Collaboration for Trajectory Collection
Lin Wu, Shu Yao, Yuheng Wang +11
Training multi-agent collaboration requires trajectories of checking, feedback, and repair, yet task-solving rollouts may end in immediate approval or repeat unsuccessful attempts…
CogDual: Enhancing Dual Cognition of LLMs via Reinforcement Learning with Implicit Rule-Based Rewards
Cheng Liu, Yifei Lu, Fanghua Ye +5
Role-Playing Language Agents (RPLAs) have emerged as a significant application direction for Large Language Models (LLMs). Existing approaches typically rely on prompt engineering…
CodeTool: Enhancing Programmatic Tool Invocation of LLMs via Process Supervision
Yifei Lu, Fanghua Ye, Jian Li +6
Tool invocation significantly enhances the capabilities of Large Language Models (LLMs), yet challenges persist, particularly in complex task scenarios. Current methods, such as in…
SR: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning
Ruotian Ma, Peisong Wang, Cheng Liu +6
Recent studies have demonstrated the effectiveness of LLM test-time scaling. However, existing approaches to incentivize LLMs' deep thinking abilities generally require large-scale…