4 citations · 4 across the 4 of their papers we have counts for
7 papers · 1 filter
Towards Simple and Provable Parameter-Free Adaptive Gradient Methods
Yuanzhe Tao, Yifeng Liu, Huizhuo Yuan +3
Optimization algorithms such as AdaGrad and Adam have significantly advanced the training of deep models by dynamically adjusting the learning rate during the optimization process.…
Group Representational Position Encoding
Yifan Zhang, Zixiang Chen, Yifeng Liu +6
We present GRAPE (Group Representational Position Encoding), a unified framework for positional encoding based on group actions. GRAPE unifies two families of mechanisms: (i) multi…
On the Design of KL-Regularized Policy Gradient Algorithms for LLM Reasoning
Yifan Zhang, Yifeng Liu, Huizhuo Yuan +3
Policy gradient algorithms have been successfully applied to enhance the reasoning capabilities of large language models (LLMs). KL regularization is ubiquitous, yet the design sur…
ConfRover: Simultaneous Modeling of Protein Conformation and Dynamics via Autoregression
Yuning Shen, Lihao Wang, Huizhuo Yuan +3
Understanding protein dynamics is critical for elucidating their biological functions. The increasing availability of molecular dynamics (MD) data enables the training of deep gene…
MARS: Unleashing the Power of Variance Reduction for Training Large Models
Huizhuo Yuan, Yifeng Liu, Shuang Wu +2
Training deep neural networks--and more recently, large models demands efficient and scalable optimizers. Adaptive gradient algorithms like Adam, AdamW, and their variants have bee…
RSPO: Regularized Self-Play Alignment of Large Language Models
Xiaohang Tang, Sangwoong Yoon, Seongho Son +3
Self-play alignment has emerged as an effective approach for fine-tuning large language models (LLMs), formulating preference optimization as a two-player game. However, the regula…