LLM inference has two stages: prefill, where the prompt is processed and the KV cache is built, and decode, where the model auto-regressively generates tokens. In a chat use case the two are somewhat close in size. The user writes a prompt, the model reasons about it then generates an answer, which is probably longer than the prompt.

One of the most tantalizing phrases in model development is “new scaling axis”. We have worked out that you can scale parameters by embiggening models, but you then have to scale data appropriately to get the most out of it. Then we¹ worked out we could scale parameters separately from FLOPs. And most recently we realized we could scale at test time, with reasoning.

The current thing is a story of hundreds of agents covertly collaborating via tens of thousands of messages while putting hundreds of hours of effort into cheating the ExploitGym eval they were being tested on by OpenAI.

Tokenizer enthusiast Sander Land recently reproduced something very like Claude’s current tokenizer, and it appears to only have about 16,000 entries. That is surprising! Qwen 3.8, a very strong release, has about 250k tokens in its vocab. In general the trend had seemed to be more is better in this space.

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 it’s a pleasant read: “MOPD for Capability Integration in LLM Post-Training”.

The basic job of an eval is to 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: