From the 1 of 7 linked papers with an AI index.
7 papers
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…
SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution
Zhiyuan Yao, Yuxin Chen, Zhengxi Lu +13
SkillRise introduces a reinforcement‑learning framework that lets large language model agents learn and reuse transferable skills across related tasks by curating a skill document…
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…
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…
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…
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…