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Louis Bethune

Alumni

Publications

The Design Space of Tri-Modal Masked Diffusion Models
Louis Bethune
Victor Turrisi
Bruno Kacper Mlodozeniec
Pau Rodriguez Lopez
Lokesh Boominathan
Nikhil Bhendawade
Amitis Shidani
Joris Pelemans
Theo X. Olausson
Devon Hjelm
Paul Dixon
Joao Monteiro
Pierre Ablin
Vishnu Banna
Arno Blaas
Nick Henderson
Kari Noriy
Dan Busbridge
Josh Susskind
Marco Cuturi … (see 4 more)
Irina Belousova
Luca Zappella
Russ Webb
Jason Ramapuram
Discrete diffusion models have emerged as strong alternatives to autoregressive language models, with recent work initializing and fine-tuni… (see more)ng 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, image-text, and audio-text data. We systematically analyze multimodal scaling laws, modality mixing ratios, noise schedules, and batch-size effects, and we provide optimized inference sampling defaults. Our batch-size analysis yields a novel stochastic differential equation (SDE)-based reparameterization that eliminates the need for tuning the optimal batch size as reported in recent work. This reparameterization decouples the physical batch size, often chosen based on compute constraints (GPU saturation, FLOP efficiency, wall-clock time), from the logical batch size, chosen to balance gradient variance during stochastic optimization. Finally, we pretrain a preliminary 3B-parameter tri-modal model on 6.4T tokens, demonstrating the capabilities of a unified design and achieving strong results in text generation, text-to-image tasks, and text-to-speech tasks. Our work represents the largest-scale systematic open study of multimodal discrete diffusion models conducted to date, providing insights into scaling behaviors across multiple modalities.
2025-12-31
arXiv (preprint)
doi.org
arxiv.org