1 citations · 1 across the 9 of their papers we have counts for
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ReTerm: Recovery Training for Self-Improving Terminal Agents
Wei Xie, Kaituo Feng, Shiming Fu +3
Terminal agents have shown strong abilities in complex coding tasks. However, they often struggle to recover from errors during long-running execution (e.g., command errors, faulty…
Discovering Diverse Planning Policies for Multimodal Embodied Agents with Quality-Diversity Optimization
Pengfei Xu, Yong Liu, Xiaoya Nan +2
Multimodal embodied agents are increasingly required to solve long-horizon tasks by integrating visual observations, textual goals, and interaction history into closed-loop decisio…
Structure-Conditioned Actor-Critic Branches for Quality-Diversity Reinforcement Learning
Lianrong Zuo, Peilan Xu, Yong Liu +1
Quality-diversity reinforcement learning (QD-RL) aims to construct policy repertoires that contain both high-performing and behaviorally diverse policies. Existing QD-RL methods ma…
AsyncLane: Decoupling Refinement from Advancement in Diffusion Language Model Decoding
Yingxuan Ren, Yuxuan Lou, Yong Liu +4
Block-wise semi-autoregressive decoding is the standard inference paradigm for diffusion large language models (DLMs), but it imposes a strict dependency between blocks: the next b…
Statistical Priors for Implicit Preferences: Decoupling Skill Selection as a Local Harness in Personal Agents
Zeyu Gan, Huayi Tang, Yong Liu
As Large Language Model (LLM) capabilities advance, locally deployed personal agents relying on API-based remote models and external skills have emerged as a novel paradigm. With t…
Capabilities and Fundamental Limits of Latent Chain-of-Thought
Jiaxuan Zou, Yaozhong Xiong, Yong Liu
Latent Chain-of-Thought (Latent CoT) models promise efficient reasoning via continuous representations, yet exhibit puzzling performance inconsistencies: excelling at exploration (…
How Does Personalized Memory Shape LLM Behavior? Benchmarking Rational Preference Utilization in Personalized Assistants
Xueyang Feng, Weinan Gan, Xu Chen +2
Large language model (LLM)-powered assistants have recently integrated memory mechanisms that record user preferences, leading to more personalized and user-aligned responses. Howe…
Towards a Theoretical Understanding to the Generalization of RLHF
Zhaochun Li, Mingyang Yi, Yue Wang +2
Reinforcement Learning from Human Feedback (RLHF) and its variants have emerged as the dominant approaches for aligning Large Language Models with human intent. While empirically e…