4 citations · 11 across the 9 of their papers we have counts for
11 papers
Learning Self-Orchestration in Decentralized Agent Swarms
Guowei Xu, Zhenting Qi, Amani Kiruga +2
The capabilities of an agent swarm depend on both its individual members and how they coordinate. Yet existing multi-agent systems often train agents within fixed orchestration str…
Economy of Minds: Emerging Multi-Agent Intelligence with Economic Interactions
Zhenting Qi, Huangyuan Su, Ao Qu +13
How can a population of agents self-orchestrate and self-adapt into stronger collective intelligence without centralized control? Inspired by Friedrich Hayek's economic theory of d…
Self-Improving Language Models with Bidirectional Evolutionary Search
Guowei Xu, Zhenting Qi, Huangyuan Su +4
Search has been proposed as an effective method for self-improving language models and agentic systems, both for post-training sample generation and for inference. However, widely…
Sparse Reward Subsystem in Large Language Models
Guowei Xu, Mert Yuksekgonul, James Zou
Recent studies show that LLM hidden states encode reward-related information, such as answer correctness and model confidence. However, existing approaches typically fit black-box…
GIFT: Guided Importance-Aware Fine-Tuning for Diffusion Language Models
Guowei Xu, Wenxin Xu, Jiawang Zhao +1
Diffusion models have recently shown strong potential in language modeling, offering faster generation compared to traditional autoregressive approaches. However, applying supervis…
metaTextGrad: Automatically optimizing language model optimizers
Guowei Xu, Mert Yuksekgonul, Carlos Guestrin +1
Large language models (LLMs) are increasingly used in learning algorithms, evaluations, and optimization tasks. Recent studies have shown that using LLM-based optimizers to automat…
LLaVA-CoT: Let Vision Language Models Reason Step-by-Step
Guowei Xu, Peng Jin, Ziang Wu +4
Large language models have demonstrated substantial advancements in reasoning capabilities. However, current Vision-Language Models (VLMs) often struggle to perform systematic and…
MENTOR: Mixture-of-Experts Network with Task-Oriented Perturbation for Visual Reinforcement Learning
Suning Huang, Zheyu Zhang, Tianhai Liang +6
Visual deep reinforcement learning (RL) enables robots to acquire skills from visual input for unstructured tasks. However, current algorithms suffer from low sample efficiency, li…
ACE : Off-Policy Actor-Critic with Causality-Aware Entropy Regularization
Tianying Ji, Yongyuan Liang, Yan Zeng +7
The varying significance of distinct primitive behaviors during the policy learning process has been overlooked by prior model-free RL algorithms. Leveraging this insight, we explo…
DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization
Guowei Xu, Ruijie Zheng, Yongyuan Liang +12
Visual reinforcement learning (RL) has shown promise in continuous control tasks. Despite its progress, current algorithms are still unsatisfactory in virtually every aspect of the…
Can Pre-Trained Text-to-Image Models Generate Visual Goals for Reinforcement Learning?
Jialu Gao, Kaizhe Hu, Guowei Xu +1
Pre-trained text-to-image generative models can produce diverse, semantically rich, and realistic images from natural language descriptions. Compared with language, images usually…