6 papers
Credit Where It Matters: Dependency-Aware Policy Optimization for Terminal Agents
Yu Li, Guangfeng Cai, Long-Fei Li +5
Terminal-using agents benefit from reinforcement learning (RL) in coding, debugging, and other multi-step terminal tasks. In these tasks, later commands often depend on information…
Choosing Before Acting: Comparative Value Estimation for Long-Horizon Tool-Use Agents
Yu Li, Zheng Zhang, Xin Liu +3
Large language models (LLMs) rely on long-horizon tool invocation sequences for complex tasks, where each invocation can alter the task state and condition subsequent decisions. In…
SCA: Spatial Credit Assignment for Reinforcement Learning of GUI Agents
Shengtian Yang, Ziyu Xiong, Kaibing Yang +6
GUI agents automate tasks on digital devices by grounding language instructions in visual interfaces. Existing group-relative reinforcement learning improves GUI action prediction…
Learning from All-Failed Groups via First-Visit Coverage in Long-Horizon Agentic Tasks
Kaibing Yang, Guangfeng Cai, Shengtian Yang +6
Group-based policy optimization has been increasingly used to train large language model (LLM) agents from sparse outcome rewards by comparing trajectories or steps within a group.…
Beyond Next-Observation Prediction: Agent-Authored World Modeling for Sequential Decision Making
Guangfeng Cai, Kaibing Yang, Shuo He +4
Recent studies on world modeling for Large Language Model (LLM) agents typically formulate the learning objective as next-observation prediction. However, this objective ties super…
PhGPO: Pheromone-Guided Policy Optimization for Long-Horizon Tool Planning
Yu Li, Guangfeng Cai, Shengtian Yang +5
Recent advancements in Large Language Model (LLM) agents have demonstrated strong capabilities in executing complex tasks through tool use. However, long-horizon multi-step tool pl…