Reinforcement learning (RL) has emerged as a key paradigm for improving large language model (LLM) agents, enabling stronger reasoning, planning, and tool-use in interactive environments. However, existing agent RL methods remain fundamentally constrained by an on-policy reasoning bottleneck: when tasks exceed the mode...
Xi Chen, Rui-Xue Ding, Wang Zhigaoyuan et al.· 0 citations
This work proposes ArenaFlow, a hierarchical credit propagation framework for open-ended agent reinforcement learning that leverages tournament-based relative ranking to derive trajectory-level reward signals and propagates trajectory-level advantages to high-confidence pivotal steps according to tournament survival de...
Qiang Zhang, Rui-Xue Ding, Fanrui Zhang et al.· 1 citation
ARISE-RL, a novel full-cycle self-evolution framework that couples a task/rubric Generator and a reasoning Solver through rubric-mediated co-evolution, is proposed and ECR-Bench, an expert-calibrated rubric benchmark suite covering single-tool deep research and multi-tool travel planning is presented.
Fan-Rui Zhang, Rui-Xue Ding, Qiang Zhang et al.· 0 citations
OrthoPilot is a clinical artificial intelligence system powered by a large language model (LLM) that integrates hospital data streams with authoritative external knowledge for continuous musculoskeletal care and autonomously retrieves real-time imaging, laboratory, pathology and order data and translates evolving patie...