Continuous Diffusion Language Models (CDLM's)

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• Hacker News
• Conspiracy time: The continuous diffusion model has fruit. Frontier labs were trying to bury it with discrete diffusion distraction, because it is what they're using internally. Momentum wins, in the end.
  by nullbio
• No conspiracy. Diffusion is just a more finicky, more expensive way of generating the same tokens as autoregressive decoding.
  by ACCount37
• I wonder if we’ll get something like CDLMs for automated harness engineering, sort of piloting the LLM underneath.
  by NickNaraghi
• how do tools like hermes do this? does it just review sessions and rewrite markdown files?

  also haven't read too deep into the deepseek agent harness but the math in there was really cool. it sounded promising, at least.

  by ramon156
• Diffusion models for language felt inevitable. Imagine the creative potential once these mature beyond current limits.
  by ovin_dal
• CDLMs sound promising for smoother, more coherent text generation. Excited to see how they tackle the token-level discontinuities.
  by Marchant_hq
• A great read - as with all of Sander's diffusion posts.
  by ainch
• I love the idea of diffusion language models. I think they are potentially superior to autoregressive models, in the way that practically, considering whole systems tends to beat optimizing any one individual detail. To me, the way autoregressive models are sampled feels very fundamentally limited, and diffusion feels much more coherent by comparison.
  by LoganDark
• I feel like there is still low hanging fruit on the auto regressive LLMs; the encoder
  by vatsachak
• I would love to see models that can think at different rates and also output a thinking scratchpad alongside output text instead of before all output.

  Right now models need to rely on less legible compressed CoT to get high intelligence per token/step, but with diffusion they would just need to output more tokens per step instead.

  by 2001zhaozhao
• It’s refreshing to read something not AI generated.
  by p1esk
• Glad to hear it! My stubbornness about this makes me feel like a luddite sometimes, but the additional effort required is probably still worth it, for the time being.
  by benanne
• > [in 2020/2021] the dominance of autoregression was not as well-established as it is today: GPT-3 had turned some heads, but the ‘ChatGPT moment’ wouldn’t come until late 2022

  I disagree with this. Decoders were absolutely dominant in 2020 for chat. GPT2 was considered too dangerous to release, and I remember scrambling to get on the GPT3 waitlist. It worked.

  (The only exception I will make is encoder-decoder models which now are often done by decoder-only.)

  But what made it go mainstream was RL. RLHF at first, then other improvements like DPO that were less of a pain in the ass to set up. Adding diffusion on top of that would be an even bigger pain in the ass.

  Before ChatGPT there really wasn’t much of a concept of pre-training and post-training. It was all pre-training. Post training was what made the bots conversational and not just “continuing the thing you wrote to them”.

  So in short, diffusion never took off because it was just a more complicated way to generate tokens, and the real problem was getting tokens in the right distribution.

  by janalsncm
• > GPT2 was considered too dangerous to release

  This is how ridiculous this industry is. Regulation-seeking panic over nothing. Drama in search of a moat.

  Everything is "too dangerous". GPT2 is going to invent a time machine and break crypto and genetically engineer super rabies.

  They sell knives, guns, combustible materials, and multi-ton heavy machinery in stores. That's what's actually dangerous.

  by echelon
• I suppose what I was trying to say is that in the research community, people were still a lot more willing to entertain alternative modelling paradigms for language at that point, there was much less of a monoculture than there is today. It was really only after ChatGPT that non-autoregressive language modelling came to be seen as a fringe pursuit. Of course that is a highly subjective assessment, and my perspective is inevitably coloured by my own research interests at the time, and those of the people around me.

  For what it's worth, I don't believe post-training diffusion models (of language or otherwise) is uniquely difficult, just a lot less thoroughly explored so far.

  by benanne
• > I disagree with this. Decoders were absolutely dominant in 2020 for chat. GPT2 was considered too dangerous to release, and I remember scrambling to get on the GPT3 waitlist. It worked.

  He's not talking about decoders, he's talking about auto-regression. Before ChatGPT, the dominant paradigm was fine-tuning BERT-like models.

  > Before ChatGPT there really wasn’t much of a concept of pre-training and post-training.

  Again, people spend years just post-training BERTs in various ways.

  by matusp