Scaling Categorical Flow Maps

來源原標題

Scaling Categorical Flow Maps

原始連結

https://machinelearning.apple.com/research/scaling-categorical-flow-maps

摘要原文

research area Speech and Natural Language Processing content type paper published August 2026 Scaling Categorical Flow Maps Authors Oscar Davis†**, Anastasiia Filippova, Victor Turrisi, Amitis Shidani, Pierre Ablin, Marco Cuturi, Louis Béthune View publication Copy Bibtex 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 of generating discrete data continuously by a simple flow matching process between a Gaussian and the one-hot encoded data distribution. They have further shown the feasibility of accelerated sampling via Categorical Flow Maps (CFMs), resulting in competitive sample quality in the few-step regime. However, this method had only been evaluated at relatively modest scales (< 1B), leaving the question of its scalability completely open. In this article, we train a 1.7B-parameter base flow model on 2.1T tokens and self-distill it into a CFM that generates diverse, high-quality text in as few as 4 inference steps while maintaining near-data-level token entropy. Furthermore, we introduce a likelihood bound for CFMs in the semi-discrete setting, and show that they can be used to score the model on standard LM benchmarks, achieving results in the same range as discrete diffusion methods. Finally, we uncover some of the challenges that arise from training these models at scale, and we provide prescriptive insights on loss weighting and time scheduling. † University of Oxford ** Work done while at Apple Related readings and updates. Score Distillation of Flow Matching Models December 16, 2025 research area Computer Vision , research area Methods and Algorithms Diffusion models achieve high-quality image generation but are limited by slow iterative sampling. Distillation methods alleviate this by enabling one- or few-step generation. Flow matching, originally introduced as a distinct framework, has since been shown to be theoretically equivalent to diffusion under Gaussian assumptions, raising the question of whether distillation techniques such as score distillation transfer directly. We provide a… Read more CAR-Flow: Condition-Aware Reparameterization Aligns Source and Target for Better Flow Matching November 12, 2025 research area Computer Vision conference NeurIPS Conditional generative modeling aims to learn a conditional data distribution from samples containing data-condition pairs. For this, diffusion and flow-based methods have attained compelling results. These methods use a learned (flow) model to transport an initial standard Gaussian noise that ignores the condition to the conditional data distribution. The model is hence required to learn both mass transport and conditional injection. To ease the… Read more Discover opportunities in Machine Learning. Our research in machine learning breaks new ground every day. Work with us