The Tradeoffs of SSMs and Transformers
goombalab.github.io69 points by jxmorris12 22 days ago
69 points by jxmorris12 22 days ago
The part on tokenisation is not very convincing. Replacing BPE with characters or even bytes will not "remove tokenisation" -- atoms will still be tokens, relating to different things in different cultures/writing traditions (a "Chinese byte" is a part of a Chinese character; an "English byte" is basicaly a letter or a number) and not relating to something fundamentally linguistic. BPE can be thought of as another way of representing linguistic sequences with symbols of some kind; it provides less inductive bias into the use of language, but it is not perhaps categorically different from any kind of writing.
The point is not that tokenization is irrelevant, its that the transformer model _requires_ information dense inputs, which is derived by compressing the input space from raw characters to subwords. Give it something like raw audio or video frames, and its capabilities dramatically bottom out. That’s why even todays sota transformer models heavily preprocess media input, even going as far as doing lightweight frame importance sampling to extract the “best” parts of the video.
In the future, all of these tricks may seem quaint. “Why don’t you just pass the raw bits of the camera feed straight to the model layers?” we may say.
I'm a bit bearish on SSMs (and hybrid SSM/transformers) because the leading open weight models (DeepSeek, Qwen, Gemma, Llama) are all transformers. There's just no way none of them tried SSMs.
As Albert mentioned, the benchmarks and data we use today heavily prioritize recall. Transformers are really really good at remembering parts of the context.
Additionally, we just don’t have training data at the size and scope that exceeds today’s transformer context lengths. Most training rollouts are fairly information dense. Its not like “look at this camera feed for four hours and tell me what interesting stuff happened”, those are extremely expensive data to generate and train on.
Yes, until serious adoption I am reserved too, both on SSMs and diffusion based LLMs.
I think I agree with you. My only rebuttal would be it's this kind of thinking that's kept any leading players form trying other architectures in the first place. As far as I know, SOTA for SSM's just doesn't suggest significant enough potential upsides warrant significant R&D. Not compared to the tried and true established LLM methods. The decision might be something like: "Pay X to train a competitive LLM" vs "Pay 2X to MAYBE train a competitive SSM".
Second-generation LSTMs (xLSTM) do have leading performance on zero-shot time series forecasting: https://arxiv.org/abs/2505.23719.
I think other architectures, aside from the transformer, might lead to SOTA performance, but they remain a bit unexplored.
I mean, everyone is still using variational autoencoders for their latent flow models instead of the information bottleneck. It's because it's cheaper (in founder time) to raise 10(0)x more money instead of having to design your own algorithms and architectures for a novel idea that might work in theory, but could be a dead end six months down the line. Just look at LiquidAI. Brilliant idea, but it took them ~5 years to do all the research and another to get their first models to market... which don't yet seem to be any better than models with a similar compute requirement. I find it pretty plausible that none of the "big" LLM companies seriously tried SSMs, because they already have plenty enough money to throw at transformers, or took a quick path to get a big valuation.