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cs.CL2026
MOA: Multi-Objective Alignment for Role-Playing Agents
Chonghua Liao, Ke Wang, Yuchuan Wu +3
Role-playing agents (RPAs) require balancing multiple objectives, such as instruction following, persona consistency, and stylistic fidelity, which are not always perfectly aligned…
cs.CL2025
Agentic Reinforcement Learning with Implicit Step Rewards
Xiaoqian Liu, Ke Wang, Yuchuan Wu +4
Large language models (LLMs) are increasingly developed as autonomous agents using reinforcement learning (agentic RL) that reason and act in interactive environments. However, spa…
cs.CL2025
EPO: Explicit Policy Optimization for Strategic Reasoning in LLMs via Reinforcement Learning
Xiaoqian Liu, Ke Wang, Yongbin Li +6
Large Language Models (LLMs) have shown impressive reasoning capabilities in well-defined problems with clear solutions, such as mathematics and coding. However, they still struggl…