5 papers · 1 filter
Reasoning with Sampling: Your Base Model is Smarter Than You Think
Aayush Karan, Yilun Du
Frontier reasoning models have exhibited incredible capabilities across a wide array of disciplines, driven by posttraining large language models (LLMs) with reinforcement learning…
ReGuidance: A Simple Diffusion Wrapper for Boosting Sample Quality on Hard Inverse Problems
Aayush Karan, Kulin Shah, Sitan Chen
There has been a flurry of activity around using pretrained diffusion models as informed data priors for solving inverse problems, and more generally around steering these models u…
When does a predictor know its own loss?
Aravind Gollakota, Parikshit Gopalan, Aayush Karan +2
Given a predictor and a loss function, how well can we predict the loss that the predictor will incur on an input? This is the problem of loss prediction, a key computational task…
Blink of an eye: a simple theory for feature localization in generative models
Marvin Li, Aayush Karan, Sitan Chen
Large language models can exhibit unexpected behavior in the blink of an eye. In a recent computer use demo, a language model switched from coding to Googling pictures of Yellowsto…
Unrolled denoising networks provably learn optimal Bayesian inference
Aayush Karan, Kulin Shah, Sitan Chen +1
Much of Bayesian inference centers around the design of estimators for inverse problems which are optimal assuming the data comes from a known prior. But what do these optimality g…