Back in the dark ages of typing code into editors we were aided by squigglies under broken code, click-to-definitions links, and so on. That was powered by language servers and type checkers. Several harnesses now expose an LSP as a tool, on the reasonable premise that better code intelligence makes for a better agent.

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.

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 predict which number SolidGoldMagikarp will say next on Reddit.

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 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.

Pre-training any kind of good LLM is very, very expensive. Thankfully, we have scaling laws. Lilian Weng of Thinky writes:

Back in 2022 and 2023 there were two big branches of machine learning happening at Meta¹. 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 remedied that state of affairs by making LLMs a lot more complicated.

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.

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.

There has been a lot of drama¹ about distillation: how (closed) frontier models are being used by other labs to boost their own performance on particularly hard tasks.