3 citations · 7 across the 4 of their papers we have counts for
4 papers
Teaching Large Language Models to Reason with Reinforcement Learning
Alex Havrilla, Yuqing Du, Sharath Chandra Raparthy +6
Reinforcement Learning from Human Feedback (\textbf{RLHF}) has emerged as a dominant approach for aligning LLM outputs with human preferences. Inspired by the success of RLHF, we s…
Understanding the Effect of Noise in LLM Training Data with Algorithmic Chains of Thought
Alex Havrilla, Maia Iyer
During both pretraining and fine-tuning, Large Language Models (\textbf{LLMs}) are trained on trillions of tokens of text of widely varying quality. Both phases of training typical…
DFU: scale-robust diffusion model for zero-shot super-resolution image generation
Alex Havrilla, Kevin Rojas, Wenjing Liao +1
Diffusion generative models have achieved remarkable success in generating images with a fixed resolution. However, existing models have limited ability to generalize to different…
On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds
Biraj Dahal, Alex Havrilla, Minshuo Chen +2
Generative networks have experienced great empirical successes in distribution learning. Many existing experiments have demonstrated that generative networks can generate high-dime…