6 papers
Count Counts: Motivating Exploration in LLM Reasoning with Count-based Intrinsic Rewards
Xuan Zhang, Ruixiao Li, Zhijian Zhou +7
Reinforcement Learning (RL) has become a compelling way to strengthen the multi step reasoning ability of Large Language Models (LLMs). However, prevalent RL paradigms still lean o…
Unleashing Flow Policies with Distributional Critics
Deshu Chen, Yuchen Liu, Zhijian Zhou +2
Flow-based policies have recently emerged as a powerful tool in offline and offline-to-online reinforcement learning, capable of modeling the complex, multimodal behaviors found in…
Constraints-Guided Diffusion Reasoner for Neuro-Symbolic Learning
Xuan Zhang, Zhijian Zhou, Weidi Xu +3
Enabling neural networks to learn complex logical constraints and fulfill symbolic reasoning is a critical challenge. Bridging this gap often requires guiding the neural network's…
Guiding Diffusion Models with Reinforcement Learning for Stable Molecule Generation
Zhijian Zhou, Junyi An, Zongkai Liu +5
Generating physically realistic 3D molecular structures remains a core challenge in molecular generative modeling. While diffusion models equipped with equivariant neural networks…
Equivariant Spherical Transformer for Efficient Molecular Modeling
Junyi An, Xinyu Lu, Chao Qu +6
Equivariant Graph Neural Networks (GNNs) have significantly advanced the modeling of 3D molecular structure by leveraging group representations. However, their message passing, hea…
Equivariant Masked Position Prediction for Efficient Molecular Representation
Junyi An, Chao Qu, Yun-Fei Shi +4
Graph neural networks (GNNs) have shown considerable promise in computational chemistry. However, the limited availability of molecular data raises concerns regarding GNNs' ability…