11 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…
SRPO: Setwise Relative Policy Optimization for Multi-Agent Systems
Shengtian Yang, Ziyu Xiong, Yu Li +3
Multi-agent systems enable complex reasoning and tool use by coordinating agents that divide roles and refine candidate solutions. Existing methods typically update individual agen…
AgentBrew: Offline Tool-Use Agent Learning from Raw Real-World Trajectories
Zhiyi Lyu, Yewen Li, Longtao Zheng +7
LLM-based agents are increasingly deployed in real-world applications through tool-use APIs, yet training them for specific environments remains fundamentally difficult: real-world…
PlatformBid: An Auto-Bidding Benchmark from a Unified Advertising Platform's Perspective
Shengtian Yang, Yewen Li, Peng Jiang +4
Real-time bidding is central to computational advertising, comprising three elements: Supply Side Platform (SSP) selling ad impressions, Demand Side Platform (DSP) bidding for adve…
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…
Phase-Aware Mixture of Experts for Agentic Reinforcement Learning
Shengtian Yang, Yu Li, Shuo He +4
Reinforcement learning (RL) has equipped LLM agents with a strong ability to solve complex tasks. However, existing RL methods normally use a \emph{single} policy network, causing…
MoniTor: Exploiting Large Language Models with Instruction for Online Video Anomaly Detection
Shengtian Yang, Yue Feng, Yingshi Liu +2
Video Anomaly Detection (VAD) aims to locate unusual activities or behaviors within videos. Recently, offline VAD has garnered substantial research attention, which has been invigo…