Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution – Paper Explained
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🤯 big update to our flow map language models paper! we believe this is the future of non-autoregressive text generation.read about it in the blog: https://t.co/DfBXrYmJc8full details in the paper: https://t.co/coiNXj4ucCwe introduce a new class of continuous flow-based language models and distill them into their corresponding flow map for one-step text generation.we beat all discrete diffusion baselines at ~8x speed!v2 gives a complete theory of the flow map over discrete data, with three equiv
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Finally got to reading the whole piece and it’s a good one! One thing that bothers me how whenever discrete diffusion is mentioned Floor says that it cannot self-correct, but this only applies to pure masked diffusion with naive sampling! Most discrete diffusion models now support correction in one way or another, with uniform diffusion models having this very very implicitly (duh, it’s one of the main selling points for diffusion over AR models)Nevertheless, I enjoyed the blogpost very much and
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