Using as little computational resources (memory and/or FLOPS) as possible as an additional optimization criterion when training NNs is an interesting avenue. I think the current state of pre-trained model families is weird. Take Llama 3.1 or Segment Anything 2: you get tiny/small/medium/larger/huge models, where for each tier the model size was predefined, and they are trained somewhat (completely?) independently. This feels iffy, patchy, and like we haven't really arrived yet.

I'd want a model that scales up and down depending on the task given at inference, and a model that doesn't have a fixed size when starting the training. Shouldn't it specialize over training progress, when seeing more tokens, and grow larger where needed? Without some human fixing a size beforehand?

Self-organization is a fascinating topic to me. This last year I've been working on Self-Organizing Gaussian Splats [0]. With a lot of squinting, this lives in a similar space as the Self-Compressing Neural Networks from the link above. The idea of the Gaussians was to build on Self-Organizing Maps (lovely 90s concept, look for some GIFs if you don't know it), and use that to represent 3D scenes in a memory-efficient way. By mapping attributes into a locally smooth 2D grid. It's quite a simple algorithm, but works really well, and better than many quite complicated coding schemes. So this has me excited that we'll (re-)discover great methods in this space in the near future.

[0]: https://fraunhoferhhi.github.io/Self-Organizing-Gaussians/