3 papers
cs.CL2026
Ask-E: An Environment for Calibrated Question Generation
Sarah Pratt, Jae Sung Park, Scott Geng +1
Today, we improve models by training and evaluating them on problems at the frontier of their abilities. Creating such problems is itself a demanding task, requiring the ability to…
cs.CV2025
When Worse is Better: Navigating the compression-generation tradeoff in visual tokenization
Vivek Ramanujan, Kushal Tirumala, Armen Aghajanyan +2
Current image generation methods are based on a two-stage training approach. In stage 1, an auto-encoder is trained to compress an image into a latent space; in stage 2, a generati…
cs.CL2025
FlexOlmo: Open Language Models for Flexible Data Use
Weijia Shi, Akshita Bhagia, Kevin Farhat +20
We introduce FlexOlmo, a new class of language models (LMs) that supports (1) distributed training without data sharing, where different model parameters are independently trained…