4 papers
RetireOPD: Self-Retiring On-Policy Distillation for Agentic Reinforcement Learning
Yan Yu, Zhengxi Lu, Yizhou Liu +8
Multi-turn agents trained with reinforcement learning (RL) receive a single scalar reward per trajectory, which motivates self on-policy distillation (OPD) to supply dense token-le…
Hierarchical Reinforcement Learning with Augmented Step-Level Transitions for LLM Agents
Shuai Zhen, Yanhua Yu, Ruopei Guo +2
Large language model (LLM) agents have demonstrated strong capabilities in complex interactive decision-making tasks. However, existing LLM agents typically rely on increasingly lo…
Goal Discovery with Causal Capacity for Efficient Reinforcement Learning
Yan Yu, Yaodong Yang, Zhengbo Lu +3
Causal inference is crucial for humans to explore the world, which can be modeled to enable an agent to efficiently explore the environment in reinforcement learning. Existing rese…
Model Evolution Framework with Genetic Algorithm for Multi-Task Reinforcement Learning
Yan Yu, Wengang Zhou, Yaodong Yang +3
Multi-task reinforcement learning employs a single policy to complete various tasks, aiming to develop an agent with generalizability across different scenarios. Given the shared c…