HN Reader

Very interesting trick, using a dictionary of basis vectors which are quickly computed from a seed without storage. But the result is the same 3 or 4 bit quantization, with only a slight improvement. Their tiles are small, just 8 or 12 weights, it's why compression doesn't go too far. It would have been great if this trick lowered quantization <1 bit/weight, that would require longer tiles. Wondering what are the limits if we use a larger reservoir of cheap entropy as part of neural net architecture, even in training.

Congrats to Apple and Meta, makes sense they did the research, this will go towards efficient serving of LLMs on phones. And it's very easy to implement.

I think it would be better to just link directly to the paper [1]. It is a work by researchers at Apple and Meta.

[1] https://arxiv.org/abs/2410.10714

It sounds like they basically find part of a pseudo-random sequence that is closest to the desired data, then store the random seed and corrections (which are small so take less space).

What did Zuck mean that Llama 4 Behemoth is already the highest performing base model and hasnt even done training yet? What are the benchmarks then?

Does he mean they did pretraining but not fine tuning?

This compression approach reminds me of similarities with human knowledge transfer. In both cases, we're looking for compact representations that can reconstruct complex information.

For technical documentation, I'm experimenting with a similar concept: instead of exhaustively documenting every implementation detail, defining a minimal set of principles and architectural decisions that allow "regenerating" the complete understanding.

Current LLMs excel at expanding compressed concepts, but we're still far from finding the optimal balance between explicit knowledge (detailed documentation) and implicit knowledge (patterns and principles). Is anyone working on systems applying similar ideas to technical knowledge management?

I suspect an April fools joke.

In general, compression using PRNGs is not a thing. There might be a special exception for this case, but I somewhat doubt it. =)

A variant I have been thinking of: each parameter matrix (or block) is the sum of a random matrix (generated from a seed) and a low rank matrix (a LoRA). I'd like to experiment training from scratch in that setting.

How do you reconcile this with the (I believe) widely accepted idea that you can't meaningfully compress data using offsets into Pi?

all this and they can't launch Apple intelligence on schedule :(

This sound like compression with extra steps.. What makes this technique particular to LLM weights instead of general purpose data?

Also from October 2024 (https://arxiv.org/abs/2410.10714)