9 papers
LongHorizon-Harness: Advancing Long-Horizon Agents for Real-World Tasks
Ziyu Ma, Hailang Huang, Shun Zou +5
Large language model (LLM) agents increasingly undertake long-horizon tasks that require sustained reasoning, tool use, and revision across many interdependent steps. However, exis…
Towards High-Resolution Visual Perception via Hierarchical Entity Exploration
Ziyu Ma, Shidong Yang, Yuxiang Ji +5
High-resolution (HR) image perception remains a key challenge in multimodal large language models (MLLMs), as fine-grained details are often lost when the image is processed as a w…
ReSum: Synergizing LLM Reasoning and Summarization with Reinforcement Learning
Xucong Wang, Ziyu Ma, Yong Wang +5
Reinforcement Learning with Verifiable Rewards (RLVR) is a central technique for improving long-horizon reasoning in Large Language Models (LLMs). However, existing RLVR methods of…
APPO: Agentic Procedural Policy Optimization
Xucong Wang, Ziyu Ma, Yong Wang +5
Recent advances in agentic Reinforcement Learning (RL) have substantially improved the multi-turn tool-use capabilities of large language model agents. However, most existing metho…
Role-Agent: Bootstrapping LLM Agents via Dual-Role Evolution
Xucong Wang, Ziyu Ma, Shidong Yang +4
Although Large Language Model (LLM) agents have demonstrated strong performance on complex tasks, their learning is often limited by inefficient interaction feedback and static tra…
Learning Agentic Policy from Action Guidance
Yuxiang Ji, Zengbin Wang, Yong Wang +6
Agentic reinforcement learning (RL) for Large Language Models (LLMs) critically depends on the exploration capability of the base policy, as training signals emerge only within its…