Forward-Learned Discrete Diffusion: Learning how to noise to denoise faster

2026-05-19 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Advanced, extended

Summary

Forward-Learned Discrete Diffusion (FLDD) is a novel framework designed to enhance the efficiency of discrete diffusion models by enabling few-step generation without compromising sample quality. Traditional discrete diffusion models often require a large number of computationally expensive sampling steps due to the fixed, factorized nature of their generative (reverse) processes, which struggles to match complex target distributions in few steps. FLDD addresses this by introducing a learnable, non-Markovian forward (noising) process with learnable marginal and posterior distributions. This allows the forward process to adaptively define a target distribution that is compatible with the efficient, factorized generative process. The model trains all parameters end-to-end using a standard variational objective, employing a relaxed warm-up phase with Concrete distributions before switching to the REINFORCE method for unbiased gradient estimation. Experiments on ROCStories, QM9, ZINC250k, and Binarized MNIST demonstrate that FLDD achieves comparable sample quality to conventional discrete diffusion models at 100 steps, while maintaining high quality even when reducing sampling steps to 10, significantly improving the quality-latency trade-off.

Key takeaway

For NLP Engineers and Research Scientists working with discrete generative models, FLDD offers a compelling approach to significantly reduce inference time without sacrificing output quality. By adopting a learnable forward process, you can achieve high-fidelity samples in as few as 10 steps, a substantial improvement over traditional discrete diffusion models. Consider integrating FLDD to optimize the trade-off between sample quality and computational cost in your generative applications, especially for text and molecular generation.

Key insights

Learning the forward noising process in discrete diffusion enables few-step, high-quality generation.

Principles

• Flexible forward processes can align with fixed reverse processes.
• Non-Markovian forward chains can define factorized targets.

Method

FLDD uses a learnable, non-Markovian forward process with factorized marginals and Maximum Coupling posteriors, trained end-to-end with a variational objective, using a Concrete distribution warm-up before REINFORCE.

In practice

• Apply FLDD to reduce sampling steps in discrete generative tasks.
• Use learnable masking schedules for improved image generation.

Topics

• Discrete Diffusion Models
• Learnable Forward Process
• Non-Markovian Diffusion
• Few-Step Generation
• Variational Training

Best for: Research Scientist, NLP Engineer, Computer Vision Engineer, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.