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PaperclipPerfector  7d ago (text post)   34944 thread views

Culture War Roundup for the week of January 20, 2025

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This is implausible for at least three reasons.

  1. We have their base model. It's very strong on standard benchmarks like Pile loss, ie predicting next tokens in some large corpus of natural text. It's just generically well-trained. You can't accelerate this with OpenAI slop and end up winning on money.
  2. The math checks out. Yes it's a feat of engineering to actually make such a cluster work but the shape of the model + 15T tokens do work out to this number of FLOPs an therefore GPU-hours. If they needed much more GPU-hours, that'd imply pathetically low FLOPs utilization.
  3. Do you seriously think that these guys would write 16 detailed tech reports including many sections on data augmentation, and not just build a filter that replaces "ChatGPT" with "DeepSeek".

Yeah on reflection and on actually reading the DeepSeekv3 technical report (here for anyone who's curious) you're right and I no longer believe my crackpot hypothesis.

1: We have their base model. [...] You can't accelerate this with OpenAI slop and end up winning on money.

I bet you could accelerate this at all with OpenAI slop, just because "token + top 5 logprobs" will generate a more precise gradient than "token alone". But that speedup would be less than you could get by using an even-more-precise loss signal by distilling the DeepSeekV2 model that they definitely already had, so "cheat by mimicking ChatGPT" is a strictly worse option than "mimic an open-source or internal model". And even that might not be worth the extra development time to speed up the already-pretty-fast early training stage. So yeah on reflection that part of the crackpot hypothesis just doesn't work.

2: The math checks out. Yes it's a feat of engineering to actually make such a cluster work but the shape of the model + 15T tokens do work out to this number of FLOPs an therefore GPU-hours. If they needed much more GPU-hours, that'd imply pathetically low FLOPs utilization.

Whispers through the grapevine have been that "pathetically low FLOPs utilization" has been pretty much par for the course for the past couple years. Whereas their technical report contains a whole bunch of "we adapted our code to the very specific performance characteristics of the GPUs we actually had, rather than the GPUs we wished we had". Section 3.3.2 of the technical report in particular is impressive in this regard (and is even more impressive in the implications, since that's a particularly legible and self-contained tricky problem, but the team likely solved dozens of other less-publishable problems of similar difficulty with a team of just 139 people).

3: Do you seriously think that these guys would write 16 detailed tech reports including many sections on data augmentation, and not just build a filter that replaces "ChatGPT" with "DeepSeek".

I sure do think that they wouldn't have done that particular filter step (if nothing else, because I would expect that to have a different failure mode where it talks about how OpenAI's DeepSeek model was released in November 2022, and that different failure mode would have shown up on Twitter and I have not seen it).

I've been sloppy with my last argument. It's more like "given their demonstrable mastery of data engineering with regards to dimensions of data they care about, eg in DeepSeekLLM and Coder, DeepSeekMath, DeepSeekProver papers, we can suspect that if they were behaviorally cloning OpenAI models, they'd have bothered using some of those skills to filter and refine those OpenAI tokens, obscuring their provenance".

Regardless, all those papers are gems and recommended reading. They're also astonishingly well written for pure Mainland effort.

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