1 citations · 1 across the 8 of their papers we have counts for
8 papers
Continuous-Time Quantum Walks based Graph Neural Network
Yuliang Zhan, Zefeng Gao, Jian Li +2
Graph Neural Networks (GNNs) are widely used on graph-structured data, but most suffer from two key weaknesses. First, message passing behaves as a low-pass filter under the homoph…
Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning
Xinyu Tang, Qianggang Cao, Yurou Liu +13
Reinforcement learning with verifiable rewards without human-annotated data, often referred to as zero RL, has emerged as a powerful paradigm for eliciting chain-of-thought reasoni…
GraphPO: Graph-based Policy Optimization for Reasoning Models
Yuliang Zhan, Xinyu Tang, Jian Li +7
Reinforcement Learning with Verifiable Rewards (RLVR) has become a standard paradigm for enhancing the capability of large reasoning models. RLVR typically samples responses indepe…
CloDS: Visual-Only Unsupervised Cloth Dynamics Learning in Unknown Conditions
Yuliang Zhan, Jian Li, Wenbing Huang +2
Deep learning has demonstrated remarkable capabilities in simulating complex dynamic systems. However, existing methods require known physical properties as supervision or inputs,…
Rethinking Sample Polarity in Reinforcement Learning with Verifiable Rewards
Xinyu Tang, Yuliang Zhan, Zhixun Li +5
Large reasoning models (LRMs) are typically trained using reinforcement learning with verifiable reward (RLVR) to enhance their reasoning abilities. In this paradigm, policies are…
L2V-CoT: Cross-Modal Transfer of Chain-of-Thought Reasoning via Latent Intervention
Yuliang Zhan, Xinyu Tang, Han Wan +3
Recently, Chain-of-Thought (CoT) reasoning has significantly enhanced the capabilities of large language models (LLMs), but Vision-Language Models (VLMs) still struggle with multi-…
SlotPi: Physics-informed Object-centric Reasoning Models
Jian Li, Wan Han, Ning Lin +8
Understanding and reasoning about dynamics governed by physical laws through visual observation, akin to human capabilities in the real world, poses significant challenges. Current…
Over-parameterized Student Model via Tensor Decomposition Boosted Knowledge Distillation
Yu-Liang Zhan, Zhong-Yi Lu, Hao Sun +1
Increased training parameters have enabled large pre-trained models to excel in various downstream tasks. Nevertheless, the extensive computational requirements associated with the…