that being said, I do very much believe that computational efficiency of models is going to go up [correction] drastically over the coming months, which does pose interesting questions over nvidia's throne
*previously miswrote and said computational efficiency will go down
I don't know what's so special about this paper.
- They claim to use MLA to reduce KV cache by 90%. Yeah, Deepseek invented that for Deepseek V2 (and also V3 and Deepseek R1 etc)
- They claim to use a hybrid linear attention architecture. So does Deepseek V3.2 and that was weeks ago. Or Granite 4, if you want to go even further back. Or Kimi Linear. Or Qwen3-Next.
- They claimed to save a lot of money not doing a full pre-train run for millions of dollars. Well, so did Deepseek V3.2... Deepseek hasn't done a full $5.6mil full pretraining run since Deepseek V3 in 2024. Deepseek R1 is just a $294k post train on top of the expensive V3 pretrain run. Deepseek V3.2 is just a hybrid linear attention post-train run - i don't know the exact price, but it's probably just a few hundred thousand dollars as well.
Hell, GPT-5, o3, o4-mini, and gpt-4o are all post-trains on top of the same expensive pre-train run for gpt-4o in 2024. That's why they all have the same information cutoff date.
I don't really see anything new or interesting in this paper that isn't already something Deepseek V3.2 has already sort of done (just on a bigger scale). Not exactly the same, but is there anything amazingly new that's not in Deepseek V3.2?
SSMs have a fixed-size state space, so on their own they'll never going be able to recite a whole file of your code in a code-editing session for example. But if much of what an LLM is doing isn't long-distance recall, you might be able to get away with only giving some layers full recall capability, with other layers manipulating the info already retrieved (plus whatever's in their own more limited memory).
I think Kimi Linear Attention and Qwen3-next are both doing things a little like this: most layers' attention/memory doesn't grow with context size. Another approach, used in Google's small open Gemma models, is to give some layers only 'local' attention (most recent N tokens) and give a few 'full' (whole context window) attention. I guess we're seeing how those approaches play out and how different tricks can be cobbled together.
There can potentially be a moneyball aspect to good model architecture. Even if on its own using space-saving attention mechanisms in some layers of big models cost something in performance, their efficiency could allow you to 'spend' more elsewhere (more layers or more params or such) to end with overall better performance at a certain level of resources. Seems like it's good to have experiments with many different approaches going on.