8 papers
Esoteric Language Models: A Family of Any-Order Diffusion LLMs
Subham Sekhar Sahoo, Zhihan Yang, Yash Akhauri +7
Diffusion-based language models offer a compelling alternative to autoregressive (AR) models by enabling parallel and controllable generation. Within this family, Masked Diffusion…
Scaling Beyond Masked Diffusion Language Models
Subham Sekhar Sahoo, Jean-Marie Lemercier, Zhihan Yang +4
Diffusion language models are a promising alternative to autoregressive models due to their potential for faster generation. Among discrete diffusion approaches, Masked diffusion c…
Remasking Discrete Diffusion Models with Inference-Time Scaling
Guanghan Wang, Yair Schiff, Subham Sekhar Sahoo +1
Part of the success of diffusion models stems from their ability to perform iterative refinement, i.e., repeatedly correcting outputs during generation. However, modern masked disc…
The Diffusion Duality
Subham Sekhar Sahoo, Justin Deschenaux, Aaron Gokaslan +3
Uniform-state discrete diffusion models hold the promise of fast text generation due to their inherent ability to self-correct. However, they are typically outperformed by autoregr…
Discrete Diffusion for Generative Modeling of Text-Aligned Speech Tokens
Pin-Jui Ku, He Huang, Jean-Marie Lemercier +3
This paper introduces a discrete diffusion model (DDM) framework for text-aligned speech tokenization and reconstruction. By replacing the auto-regressive speech decoder with a dis…
Simple Guidance Mechanisms for Discrete Diffusion Models
Yair Schiff, Subham Sekhar Sahoo, Hao Phung +7
Diffusion models for continuous data gained widespread adoption owing to their high quality generation and control mechanisms. However, controllable diffusion on discrete data face…