1 citations · 1 across the 6 of their papers we have counts for
8 papers
Steering Black-Box Large Language Model Agents with Pitfall Anticipation World Model
Jiongxiao Wang, Han Ding, Youzhi Luo +4
Frontier black-box Large Language Model (LLM) agents are proficient at long-horizon reasoning and precise action generation. However, when it comes to a specific agentic task, they…
SynCo: Data Synthesis Co-Training for Self-Evolving LLMs via Multi-Agent Reinforcement Learning
Wei Yang, Shawn Li, Yuehan Qin +8
Self-evolving LLM agents promise to improve autonomously through continual interaction and learning, reducing their dependence on manually curated supervision. Realizing this promi…
Prism-GRPO: Faster VLA Policy Optimization via Splitting Same-outcome Groups
Zeyun Deng, Yuzhe Lu, Yawei Wang +6
GRPO is increasingly used for reinforcement learning of vision-language-action (VLA) policies because, unlike PPO, it does not require training a critic. This simplification comes…
A Sober Look at Agentic Misalignment in Automated Workflows
Wenqian Ye, Bo Yuan, Zhichao Xu +4
We study a class of emergent misalignment in multi-agent systems (MAS), with a focus on automated workflows, which we refer to agentic misalignment. Although these systems can solv…
CLEAR: Context Augmentation from Contrastive Learning of Experience via Agentic Reflection
Linbo Liu, Guande Wu, Han Ding +7
Large language model agents rely on effective model context to obtain task-relevant information for decision-making. Many existing context engineering approaches primarily rely on…
Reinforcement Learning for Self-Improving Agent with Skill Library
Jiongxiao Wang, Qiaojing Yan, Yawei Wang +6
Large Language Model (LLM)-based agents have demonstrated remarkable capabilities in complex reasoning and multi-turn interactions but struggle to continuously improve and adapt wh…
SALT: Step-level Advantage Assignment for Long-horizon Agents via Trajectory Graph
Jiazheng Li, Yawei Wang, David Yan +5
Large Language Models (LLMs) have demonstrated remarkable capabilities, enabling language agents to excel at single-turn tasks. However, their application to complex, multi-step, a…
RECON: Reasoning with Condensation for Efficient Retrieval-Augmented Generation
Zhichao Xu, Minheng Wang, Yawei Wang +4
Search agents trained with reinforcement learning (RL) interleave reasoning with tool calls in a multi-turn, tool-integrated reasoning (TIR) loop, where each tool invocation return…