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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 görüntüleme • 8 ay önce •via X (Twitter)

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Shreyansh profil fotoğrafı

Shreyansh8 ay önce

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,,, profil fotoğrafı

Gareth Jax,,,7 ay önce

@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 →

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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“Next-token prediction” just got a serious rival 🤯 Ant Group just dropped LLaDA 2.1, and it challenges the dominant paradigm of LLMs. Unlike most models that generate one token at a time, LLaDA 2.1 uses diffusion to generate blocks of text in parallel. Why this changes everything: → Global Planning: It effectively sees the "future" while writing the "past" → Parallel Generation: It generates chunks in parallel, not sequentially, being much faster → Massive Efficiency: 16B MoE architecture that only uses ~1.4B active parameters per step. 100% Open Source.

Alvaro Cintas

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