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PaperclipPerfector  7d ago (text post)   34962 thread views

Culture War Roundup for the week of January 20, 2025

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That's the point: He is invited NOW, after "suddenly" shipping a model on Western Frontier level.

7 months ago I have said:

We don't understand the motivations of Deepseek and the quant fund High-Flyer that's sponsoring them, but one popular hypothesis is that they are competing with better-connected big tech labs for government support, given American efforts in cutting supply of chips to China. After all, the Chinese also share the same ideas of their trustworthiness, and so you have to be maximally open to Western evaluators to win the Mandate of Heaven.

Presumably, this was true and this is him succeeding. As I note here.

As for how it used to be when he was just another successful quant fund CEO with some odd interests, I direct you to this thread:

The Chinese government started to crack down on the quant trading industry amid economic slowdown, a housing crisis and a declining stock market index.

The CSI300 (Chinese Blue Chip Index) reached an all-time low. They blamed high frequency traders for exploiting the market and causing the selloff.

  • Banned a quant competitor from trading for 3 days
  • Banned another from opening index futures for 12 months
  • Required strategy disclosures before trading
  • Threatened to increase trading costs 10x to destroy the industry High-Flyer faced extinction. (High-Flyer’s funds have been flat/down since 2022 and has trailed the index by 4% since 2024)

so I stand by my conjectures.

they still have a good model, though I wouldn't exactly trust the headline training cost numbers since there's no way to verify how many tokens they really trained the model on

So you recognize that the run itself as described is completely plausible, underwhelming even. Correct.

What exactly is your theory then? That it's trained on more than 15T tokens? 20T, 30T, what number exactly? Why would they need to?

Here's a Western paper corroborating their design choices [Submitted on 12 Feb 2024]:

Our results suggest that a compute-optimal MoE model trained with a budget of 1020 FLOPs will achieve the same quality as a dense Transformer trained with a 20× greater computing budget, with the compute savings rising steadily, exceeding 40× when budget of 1025 FLOPs is surpassed (see Figure 1). … when all training hyper-parameters N, D, G are properly selected to be compute-optimal for each model, the gap between dense and sparse models only increases as we scale… Higher granularity is optimal for larger compute budgets.

Here's DeepSeek paper from a month prior:

Leveraging our architecture, we subsequently scale up the model parameters to 16B and train DeepSeekMoE 16B on a large-scale corpus with 2T tokens. Evaluation results reveal that with only about 40% of computations, DeepSeekMoE 16B achieves comparable performance with DeepSeek 7B (DeepSeek-AI, 2024), a dense model trained on the same 2T corpus. We also compare DeepSeekMoE with open source models and the evaluations demonstrate that DeepSeekMoE 16B consistently outperforms models with a similar number of activated parameters by a large margin, and achieves comparable performance with LLaMA2 7B (Touvron et al., 2023b), which has approximately 2.5 times the activated parameters. Evaluation results show that DeepSeekMoE Chat 16B also achieves comparable performance with DeepSeek Chat 7B and LLaMA2 SFT 7B in the chat setting. Encouraged by these results, we further undertake a preliminary endeavor to scale up DeepSeekMoE to 145B. The experimental results still validate its substantial advantages over the GShard architecture consistently. In addition, it shows performance comparable with DeepSeek 67B, using only 28.5% (maybe even 18.2%) of computations.

As expected they kept scaling and increasing granularity. As a result, they predictably reach roughly the same loss on the same token count as LLaMA-405B. Their other tricks also helped with downstream performance.

There is literally nothing to be suspicious about. It's all simply applying best practices and not fucking up, almost boring. The reason people are so appalled is that American AI industry is bogged down in corruption covered with tasteless mythology, much like Russian military pre Feb 2022.

It's all simply applying best practices and not fucking up, almost boring.

It's pretty weird: there's nothing there that any of the big labs in the West should have trouble replicating a hundred times over, and DeepSeek still managed to make something that can trade blows with them (and subjectively win, more often than not).

Might it really be just clarity of purpose leading to focusing on what matters? About a week ago, I remember Claude lecturing me, apropos of nothing, a bit about how it's best to buy from local bookstores instead of online retailers in response to me asking about what kind of textbook would be used for a particular course. I've not experienced DeepSeek doing anything even close to that, and it makes me wonder if the extraneous post-training being lathered on is the real difference here. Western models get distracted and are pulled in a thousand different directions, while DeepSeek can focus on what's relevant.

I'm not impressed by "they work in a field censured by the state, therefore they have no state connections". Jack Ma was also (personally!) censured by the state, and he's certainly connected. In the US, the DOJ seeks to break up Google. The Sacklers got sued into oblivion. All these people are connected - getting rekt by government action is an occupational hazard of being Noticed by the government, and those who are Noticed typically try to ingratiate themselves.

Thanks for the links about the model training, that's interesting reading.

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