Victor Turrisi

Scaling Categorical Flow Maps

Oscar Davis, Anastasiia Filippova, Pierre Ablin, Victor Turrisi et autres

Continuous diffusion and flow matching models could represent a powerful alternative to autoregressive approaches for language modelling (LM), as they unlock a host of advantages currently reserved for continuous modalities, including accelerated sampling and tilting. Recently, several works have demonstrated the possibility …

Scaling Categorical Flow Maps

Oscar Davis, Anastasiia Filippova, Pierre Ablin, Victor Turrisi et autres

Continuous diffusion and flow matching models could represent a powerful alternative to autoregressive approaches for language modelling (LM), as they unlock a host of advantages currently reserved for continuous modalities, including accelerated sampling and tilting. Recently, several works have demonstrated the possibility …

il, gb (code pays fourni par la source)

The Design Space of Tri-Modal Masked Diffusion Models

Louis Bethune, Victor Turrisi, Bruno Mlodozeniec, Pau Rodriguez Lopez et autres

Discrete diffusion models have emerged as strong alternatives to autoregressive language models, with recent work initializing and fine-tuning a base unimodal model for bimodal generation. Diverging from previous approaches, we introduce the first tri-modal masked diffusion model pretrained from scratch on text, …

The Design Space of Tri-Modal Masked Diffusion Models

Louis Bethune, Victor Turrisi, Bruno Mlodozeniec, Pau Rodriguez Lopez et autres

Discrete diffusion models have emerged as strong alternatives to autoregressive language models, with recent work initializing and fine-tuning a base unimodal model for bimodal generation. Diverging from previous approaches, we introduce the first tri-modal masked diffusion model pretrained from scratch on text, …

Learning Unmasking Policies for Diffusion Language Models

Metod Jazbec, Theo X. Olausson, Louis Béthune, Pierre Ablin et autres

Diffusion (Large) Language Models (dLLMs) now match the downstream performance of their autoregressive counterparts on many tasks, while holding the promise of being more efficient during inference. One critical design aspect of dLLMs is the sampling procedure that selects which tokens to …