Discrete Flow Matching
<|reference_start|>Discrete Flow Matching: Despite Flow Matching and diffusion models having emerged as powerful generative paradigms for continuous variables such as images and videos, their application to high-dimensional discrete data, such as language, is still limited. In this work, we present Discrete Flow Matching, a novel discrete flow paradigm designed specifically for generating discrete data. Discrete Flow Matching offers several key contributions: (i) it works with a general family of probability paths interpolating between source and target distributions; (ii) it allows for a generic formula for sampling from these probability paths using learned posteriors such as the probability denoiser ($x$-prediction) and noise-prediction ($\epsilon$-prediction); (iii) practically, focusing on specific probability paths defined with different schedulers considerably improves generative perplexity compared to previous discrete diffusion and flow models; and (iv) by scaling Discrete Flow Matching models up to 1.7B parameters, we reach 6.7% Pass@1 and 13.4% Pass@10 on HumanEval and 6.7% Pass@1 and 20.6% Pass@10 on 1-shot MBPP coding benchmarks. Our approach is capable of generating high-quality discrete data in a non-autoregressive fashion, significantly closing the gap between autoregressive models and discrete flow models.<|reference_end|>
@article{gat2024discrete,
    title={Discrete Flow Matching},
    author={Itai Gat, Tal Remez, Neta Shaul, Felix Kreuk, Ricky T. Q. Chen,
  Gabriel Synnaeve, Yossi Adi, Yaron Lipman},
    journal={arXiv preprint arXiv:2407.15595},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15595},
    primaryClass={cs.LG cs.AI}
}