collaborators

7 papers

cs.AI2026

EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning

Zishan Xu, Zhiyuan Yao, Yuxin Chen +9

Training large language model agents for long-horizon tool use typically relies on interactions with real or synthesized executable environments, whose construction and verificatio…

cs.LG2026

SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution

Zhiyuan Yao, Yuxin Chen, Zhengxi Lu +13

Large language model agents often encounter related yet distinct tasks that share reusable solution patterns. Yet standard agentic reinforcement learning treats tasks as independen…

cs.AI2026

Skill1: Unified Evolution of Skill-Augmented Agents via Reinforcement Learning

Yaorui Shi, Yuxin Chen, Zhengxi Lu +6

A persistent skill library allows language model agents to reuse successful strategies across tasks. Maintaining such a library requires three coupled capabilities. The agent selec…

cs.AI2026

AJ-Bench: Benchmarking Agent-as-a-Judge for Environment-Aware Evaluation

Wentao Shi, Yu Wang, Yuyang Zhao +8

As reinforcement learning continues to scale the training of large language model-based agents, reliably verifying agent behaviors in complex environments has become increasingly c…

cs.AI2026

AgentNoiseBench: Benchmarking Robustness of Tool-Using LLM Agents Under Noisy Condition

Ruipeng Wang, Yuxin Chen, Yukai Wang +9

Recent advances in large language models have enabled LLM-based agents to achieve strong performance on a variety of benchmarks. However, their performance in real-world deployment…

cs.CL2026

Learning to Self-Verify Makes Language Models Better Reasoners

Yuxin Chen, Yu Wang, Yi Zhang +9

Recent large language models (LLMs) achieve strong performance in generating promising reasoning paths for complex tasks. However, despite powerful generation ability, LLMs remain…

cs.LG2026

CoBA-RL: Capability-Oriented Budget Allocation for Reinforcement Learning in LLMs

Zhiyuan Yao, Yi-Kai Zhang, Yuxin Chen +7

Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a key approach for enhancing LLM reasoning. However, standard frameworks like Group Relative Policy Optimizatio…