11 papers
Unlocking Feature Learning in Gated Delta Networks at Scale
Yifeng Liu, Quanquan Gu
Training and scaling Large Language Models demand enormous computational resources, motivating both efficient sub-quadratic architectures and principled hyperparameter tuning metho…
Self-Distilled Policy Gradient
Yifeng Liu, Shiyuan Zhang, Yifan Zhang +1
On-policy self-distillation, where a language model conditions on privileged context to supervise its own generations, is a promising source of dense supervision for sparse-reward…
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…
Deep Delta Learning
Yifan Zhang, Yifeng Liu, Mengdi Wang +1
Transformer residual streams evolve through additive updates. Although a sufficiently expressive residual block can represent content replacement, standard architectures do not par…
A Survey of Zero-Knowledge Proof Based Verifiable Machine Learning
Zhizhi Peng, Chonghe Zhao, Taotao Wang +7
Machine learning is increasingly deployed through outsourced and cloud-based pipelines, which improve accessibility but also raise concerns about computational integrity, data priv…
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…