18 citations · 53 across the 15 of their papers we have counts for
15 papers
Almost Free State Prediction Separation
John Langford, Nathan Godey, Giovanni Monea +5
State--prediction separation (SPS) relieves a language model's hidden state of two competing burdens---summarizing the context and predicting the next token---by splitting the forw…
Full-bandwidth transformer
Xi Wang, Ziyang Cai, Zheng Zhan +5
Autoregressive transformers compute along two axes: horizontally across generated tokens, and vertically through model depth. Dense attention gives each token broad horizontal acce…
Bridging the Gap Between Latent and Explicit Reasoning with Looped Transformers
Ying Fan, Anej Svete, Kangwook Lee
Language models typically reason via explicit chain-of-thought (CoT), generating intermediate steps token-by-token. Latent CoT offers an alternative: it performs multi-step reasoni…
ReJump: A Tree-Jump Representation for Analyzing and Improving LLM Reasoning
Yuchen Zeng, Shuibai Zhang, Wonjun Kang +9
Large Reasoning Models (LRMs) are Large Language Models (LLMs) explicitly trained to generate long-form Chain-of-Thoughts (CoTs), achieving impressive success on challenging tasks…
Dion: Distributed Orthonormalized Updates
Kwangjun Ahn, Byron Xu, Natalie Abreu +5
Orthonormalized updates accelerate training, improve stability, and enable robust hyperparameter transfer, but existing methods like Muon rely on dense matrix operations that clash…
VersaPRM: Multi-Domain Process Reward Model via Synthetic Reasoning Data
Thomas Zeng, Shuibai Zhang, Shutong Wu +13
Process Reward Models (PRMs) have proven effective at enhancing mathematical reasoning for Large Language Models (LLMs) by leveraging increased inference-time computation. However,…
Looped Transformers for Length Generalization
Ying Fan, Yilun Du, Kannan Ramchandran +1
Recent work has shown that Transformers trained from scratch can successfully solve various arithmetic and algorithmic tasks, such as adding numbers and computing parity. While the…
How to Solve Contextual Goal-Oriented Problems with Offline Datasets?
Ying Fan, Jingling Li, Adith Swaminathan +2
We present a novel method, Contextual goal-Oriented Data Augmentation (CODA), which uses commonly available unlabeled trajectories and context-goal pairs to solve Contextual Goal-O…
DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models
Ying Fan, Olivia Watkins, Yuqing Du +7
Learning from human feedback has been shown to improve text-to-image models. These techniques first learn a reward function that captures what humans care about in the task and the…
Domain Generalization via Nuclear Norm Regularization
Zhenmei Shi, Yifei Ming, Ying Fan +2
The ability to generalize to unseen domains is crucial for machine learning systems deployed in the real world, especially when we only have data from limited training domains. In…
Optimizing DDPM Sampling with Shortcut Fine-Tuning
Ying Fan, Kangwook Lee
In this study, we propose Shortcut Fine-Tuning (SFT), a new approach for addressing the challenge of fast sampling of pretrained Denoising Diffusion Probabilistic Models (DDPMs). S…
Score-based Generative Modeling Secretly Minimizes the Wasserstein Distance
Dohyun Kwon, Ying Fan, Kangwook Lee
Score-based generative models are shown to achieve remarkable empirical performances in various applications such as image generation and audio synthesis. However, a theoretical un…
POEM: Out-of-Distribution Detection with Posterior Sampling
Yifei Ming, Ying Fan, Yixuan Li
Out-of-distribution (OOD) detection is indispensable for machine learning models deployed in the open world. Recently, the use of an auxiliary outlier dataset during training (also…
Model-based Reinforcement Learning for Continuous Control with Posterior Sampling
Ying Fan, Yifei Ming
Balancing exploration and exploitation is crucial in reinforcement learning (RL). In this paper, we study model-based posterior sampling for reinforcement learning (PSRL) in contin…
Efficient Model-Free Reinforcement Learning Using Gaussian Process
Ying Fan, Letian Chen, Yizhou Wang
Efficient Reinforcement Learning usually takes advantage of demonstration or good exploration strategy. By applying posterior sampling in model-free RL under the hypothesis of GP,…