8 citations · 10 across the 7 of their papers we have counts for
7 papers
NGRPO: Negative-enhanced Group Relative Policy Optimization
Gongrui Nan, Siye Chen, Jing Huang +8
RLVR has enhanced the reasoning capabilities of Large Language Models (LLMs) across various tasks. However, GRPO, a representative RLVR algorithm, suffers from a critical limitatio…
SSBE-PINN: A Sobolev Boundary Scheme Boosting Stability and Accuracy in Elliptic/Parabolic PDE Learning
Qixuan Zhou, Chuqi Chen, Tao Luo +1
Physics-Informed Neural Networks (PINNs) have emerged as a powerful framework for solving partial differential equations (PDEs), yet they often fail to achieve accurate convergence…
Quantifying Training Difficulty and Accelerating Convergence in Neural Network-Based PDE Solvers
Chuqi Chen, Qixuan Zhou, Yahong Yang +2
Neural network-based methods have emerged as powerful tools for solving partial differential equations (PDEs) in scientific and engineering applications, particularly when handling…
A priori Estimates for Deep Residual Network in Continuous-time Reinforcement Learning
Shuyu Yin, Qixuan Zhou, Fei Wen +1
Deep reinforcement learning excels in numerous large-scale practical applications. However, existing performance analyses ignores the unique characteristics of continuous-time cont…
On Residual Minimization for PDEs: Failure of PINN, Modified Equation, and Implicit Bias
Tao Luo, Qixuan Zhou
As a popular and easy-to-implement machine learning method for solving differential equations, the physics-informed neural network (PINN) sometimes may fail and find poor solutions…
Empirical Phase Diagram for Three-layer Neural Networks with Infinite Width
Hanxu Zhou, Qixuan Zhou, Zhenyuan Jin +3
Substantial work indicates that the dynamics of neural networks (NNs) is closely related to their initialization of parameters. Inspired by the phase diagram for two-layer ReLU NNs…
Towards Understanding the Condensation of Neural Networks at Initial Training
Hanxu Zhou, Qixuan Zhou, Tao Luo +2
Empirical works show that for ReLU neural networks (NNs) with small initialization, input weights of hidden neurons (the input weight of a hidden neuron consists of the weight from…