Imagine you have an army of helpers — several different Large Language Models (LLMs), each capable of handling tasks from simple queries to complex reasoning. But each helper costs something: time, compute, or actual money if you’re using an API. So the question is: Can we orchestrate these models wisely — starting from the cheapest one that might do the job, escalating only when needed — without exceeding a cost budget? ...
Imagine you’re training a massive language model — the kind that takes weeks to learn even the basics. Every training step costs time, electricity, and a small fortune. In such a world, even a tiny bump in efficiency feels like finding a way to get free coffee at work — small, but sweet. Enter SNOO – Step-K Nesterov Outer Optimizer, a clever idea that takes Nesterov momentum, a decades-old optimization trick, and applies it in a new place — outside the normal training loop. The result? Models that learn faster and more smoothly, without much extra computational cost. ...