1 citations · 1 across the 5 of their papers we have counts for
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Where Do CoT Training Gains Land in LLM based Agents?
Jingyu Liu, Zhiwen Wang, Yuxin Jing +2
Chain-of-thought (CoT) reasoning is widely used in language-model agents, but prior work has shown that verbalized CoT is not always faithful and may instead reflect post-hoc reaso…
Diagnosing Task Insensitivity in Language Agents
Jingyu Liu, Xiaopeng Wu, Kehan Chen +2
Large language models can serve as capable long-horizon agents, but their out-of-distribution (OOD) generalization remains weak. We identify a key source of this failure as task in…
Gradient Coupling: The Hidden Barrier to Generalization in Agentic Reinforcement Learning
Jingyu Liu, Xiaopeng Wu, Jingquan Peng +4
Reinforcement learning (RL) is a dominant paradigm for training autonomous agents, yet these agents often exhibit poor generalization, failing to adapt to scenarios not seen during…
Do not Abstain! Identify and Solve the Uncertainty
Jingyu Liu, Jingquan Peng, xiaopeng Wu +4
Despite the widespread application of Large Language Models (LLMs) across various domains, they frequently exhibit overconfidence when encountering uncertain scenarios, yet existin…