Essay marks Solomonoff’s 100th birthday by linking language-model pretraining to compression
ryangr · x · 2026-07-25
An essay commemorating Ray Solomonoff’s 100th birthday revisits the link between compression and intelligence.
The excerpt in the image explains pretraining as next-token prediction, then connects it to compression:
- token prediction probability can be turned into surprisal and measured in bits
- the total code length is the sum of per-token surprisal
- better next-token prediction means fewer bits are needed to encode the data
- cross-entropy training is therefore the average surprisal
The core claim is that language-model pretraining is, in effect, optimizing for compact encodings of data sequences.
Related event: Remembering Solomonoff: The Link Between LLMs and Compression(2 posts)→
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