2 papers
cs.AI2025
MARS: Co-evolving Dual-System Deep Research via Multi-Agent Reinforcement Learning
Guoxin Chen, Zile Qiao, Wenqing Wang +10
Large Reasoning Models (LRMs) face two fundamental limitations: excessive token consumption when overanalyzing simple information processing tasks, and inability to access up-to-da…
cs.LG2025
Encouraging Good Processes Without the Need for Good Answers: Reinforcement Learning for LLM Agent Planning
Zhiwei Li, Yong Hu, Wenqing Wang
The functionality of Large Language Model (LLM) agents is primarily determined by two capabilities: action planning and answer summarization. The former, action planning, is the co…