Needless text
It is a truth universally acknowledged that an airline in possession of an airplane must be in want of engines to make it go. Yet, somewhat surprisingly, they don’t really buy engines. Rolls-Royce were the notable innovator here in selling not an engine, but instead what they...
One good way to annoy a neuroscientist is to compare an LLM to the brain. It’s appealing though! There are similarities! In infancy we take a complex fusion of sensory inputs and learn to make predictions in latent space, while in pre-training a stack of Transformers learn to...
We talked about this sort of thing a bit before, but now the official Multi-Teacher On-Policy distillation paper is out, and its a pleasant read: “MOPD for Capability Integration in LLM Post-Training”. The problem MOPD is solving is composing a bunch of different capabilities...
The basic job of an eval is let you judge how good your model is on a task. If enough people use the same eval we can use it to benchmark the relative performance of multiple models on a level playing field. All good, no drama. But building good benchmarks is hard! ImageNet wa...
Pre-training any kind of good LLM is very, very expensive. Thankfully, we have scaling laws. Lilian Weng of Thinky writes: Scaling laws are one of the most critical empirical findings in deep learning. The observation is simple in form: the training loss decreases predictably...
Back in 2022 and 2023 there were two big branches of machine learning happening at Meta. The LLM work that led to Llama was a clean, smooth stack of repeated Transformer modules; the recommendation systems graphs were, by contrast, terrifying. Luckily, the industry has remedie...
When we started building LLMs, we mostly focused on them knowing things. They had information encoded in their weights, and they could spit it out when given sufficient prompts. But an agent doesn’t just need to know things; it needs to combine several kinds of knowledge. A lo...
The capabilities in a large language model emerge, mysteriously, from the training data. Everyone agrees that you start with a big pile of data, add some compute, and at the end you can vibe code. Opinions differ on what that pile of data should look like. Microsoft AI recentl...
There has been a lot of drama about distillation: how (closed) frontier models are being used by other labs to boost their own performance on particularly hard tasks. The drama is not fake, exactly. Anthropic, and recently OpenAI, have a notable lead in the agentic-coding doma...
A thing you can do is take the most performance and correctness sensitive part of your stack and just ask a chatbot to write it for you. They will sometimes get it right! Back towards the end of 2024 Ouyang et al at Stanford attempted to benchmark how often that happened with...