I was reading about the introduction of Multi-Head Latent Attention (MLA) by DeepSeek-V2 in 2024, and it got me thinking – how did this idea not come up sooner? MLA works by projecting keys and values into a latent space and performing attention there, which significantly reduces complexity. It seems like a natural next step, especially considering the trends we’ve seen in recent years.
For instance, the shift from diffusion in pixel space to latent diffusion, like in Stable Diffusion, followed a similar principle: operating in a learned latent representation for efficiency. Even in the attention world, Perceiver explored projecting queries into a latent space to reduce complexity back in 2021. So, it’s surprising that MLA didn’t appear until 2024.
Of course, we all know that in machine learning research, good ideas often don’t work out of the box without the right ‘tricks’ or nuances. Maybe someone did try something like MLA years ago, but it just didn’t deliver without the right architecture choices or tweaks.
I’m curious – did people experiment with latent attention before but fail to make it practical, until DeepSeek figured out the right recipe? Or did we really just overlook latent attention all this time, despite hints like Perceiver being out there as far back as 2021?
It’s interesting to think about how ideas evolve in the machine learning community and what it takes for them to become practical and widely adopted. If you’re interested in learning more about MLA and its potential applications, I’d recommend checking out some of the research papers and articles on the topic.