Forward-Learned Discrete Diffusion: Learning how to noise to denoise faster
Discrete diffusion models are a powerful class of generative models with strong performance across many domains. For efficiency, however, discrete diffusion typically parameterizes the generative (reverse) process with factorized distributions, which makes it difficult for the model to learn the target process in a small number of steps and necessitates a long, computationally expensive sampling procedure. To reduce the gap between the target and model distributions and enable few-step generation, we propose Forward-Learned Discrete Diffusion (FLDD), which introduces discrete diffusion with a learnable forward (noising) process. Rather than fixing a Markovian forward chain, we adopt a non...
| Authors | Grigory Bartosh, Teodora Pandeva, Sushrut Karmalkar et al. |
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| Primary category | stat.ML |
| Published | 18 May 2026 |
| Last updated at source | 18 May 2026 |
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