Research
Scaling Categorical Flow Maps
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 fo
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…
Related
- ELF: Embedded Language Flows
- Posterior Refinement: Fast Language Generation via Any-Order Flow Maps
- Flow Map Language Models: One-step Language Modeling via Continuous Denoising
Source: Apple ML Research | 2026-08-07