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Listen to Samar Khanna explain why parallel generation, rather than sequential, raises the performance ceiling for language models. Learn more about diffusion LLMs. → We're hiring:

Inception

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18,582 次观看 • 8 个月前 •via X (Twitter)

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Shreyansh 的头像

Shreyansh8 个月前

In the parallel / diffusion-style generation, does the model start from fully random tokens and iteratively replace them with new tokens at each refinement step? If so, doesn’t that effectively mean multiple token generations per position, increasing token compute compared to autoregressive decoding? Or are the correct tokens already implicitly represented in the initial state and just being refined without “new” token sampling each step? Just curious 👀.

Gareth Jax,,, 的头像

Gareth Jax,,,8 个月前

@samar_a_khanna is it multimodal ?

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Reasoning = step-by-step thinking. Usually. Diffusion reasons in parallel and refines natively. Better quality without the latency tax. Samar Khanna on how diffusion reasoning works. We're hiring →

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