> [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.
echelon 3 hours ago [-]
> 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.
slashdave 8 minutes ago [-]
Well, no. Without controls, a language model can drive sensitive individuals to violence or suicide. The idea of releasing a frontier model without RL is frightening based on what we have learned.
hodgehog11 2 hours ago [-]
I agree that this should be something that researchers reflect on. GPT-2 is one of the primary models to research on nowadays, and many recent developments have come from studying it as a test bench.
Imagine if CRISPR was considered "too dangerous" to publish because of the potential ethical ramifications, and that only a special few should be aware. It is utter self-righteousness, and it is shameful behaviour. The world cannot adjust itself to what it cannot see, so you risk greater catastrophe by keeping it secret.
The open dissemination of knowledge at every increment is the only way for society to truly deal with what is to come.
janalsncm 3 hours ago [-]
My impression at the time was they were perhaps overly cautious but this was a bunch of researchers who wanted to self-regulate. Anthropic didn’t exist, deepmind was also much less product-focused and relatively cautious. Chinese models weren’t really a factor either.
Government regulation was really not in the picture either in 2020 or 2021 tbh. The government was still trying to beat a pandemic. Some of the Biden admin eventually wanted to but it wasn’t very serious.
alightsoul 49 minutes ago [-]
why are chinese models a consideration at all?
2001zhaozhao 27 minutes ago [-]
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.
p1esk 3 hours ago [-]
It’s refreshing to read something not AI generated.
vatsachak 2 hours ago [-]
I feel like there is still low hanging fruit on the auto regressive LLMs; the encoder
ainch 56 minutes ago [-]
A great read - as with all of Sander's diffusion posts.
Marchant_hq 2 hours ago [-]
CDLMs sound promising for smoother, more coherent text generation. Excited to see how they tackle the token-level discontinuities.
1 hours ago [-]
ovin_dal 2 hours ago [-]
Diffusion models for language felt inevitable. Imagine the creative potential once these mature beyond current limits.
NickNaraghi 4 hours ago [-]
I wonder if we’ll get something like CDLMs for automated harness engineering, sort of piloting the LLM underneath.
ramon156 3 hours ago [-]
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.
pests 25 minutes ago [-]
> does it just review sessions and rewrite markdown files
Yes, same with openclaw etc. Some might have plugins to integrate with graph or vector databases besides only markdown files.
amelius 4 hours ago [-]
"Attention is all you need" should be renamed into "Attention is sufficient but not necessary".
slashdave 5 minutes ago [-]
Just about anyone building a large diffusion model is relying on transformers
p1esk 3 hours ago [-]
It’s the opposite: attention is necessary but not sufficient.
ViscountPenguin 4 hours ago [-]
A quick look at the continuous diffusion models linked in the post shows lots of transformer models still
PrinceAliHad2P 3 hours ago [-]
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6stringmerc 4 hours ago [-]
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Rendered at 02:00:10 GMT+0000 (Coordinated Universal Time) with Vercel.
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.
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.
Imagine if CRISPR was considered "too dangerous" to publish because of the potential ethical ramifications, and that only a special few should be aware. It is utter self-righteousness, and it is shameful behaviour. The world cannot adjust itself to what it cannot see, so you risk greater catastrophe by keeping it secret.
The open dissemination of knowledge at every increment is the only way for society to truly deal with what is to come.
Government regulation was really not in the picture either in 2020 or 2021 tbh. The government was still trying to beat a pandemic. Some of the Biden admin eventually wanted to but it wasn’t very serious.
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.
also haven't read too deep into the deepseek agent harness but the math in there was really cool. it sounded promising, at least.
Yes, same with openclaw etc. Some might have plugins to integrate with graph or vector databases besides only markdown files.