Intro LLMs being trained with RLVR (Reinforcement Learning from Verifiable Rewards) start off with a 'chain-of-thought' (CoT) in whatever language the LLM was originally trained on. But after a long period of training, the CoT sometimes starts to look very weird; to resemble no human language; or even to grow completely unintelligible.
Why might this happen?
I've seen a lot of speculation about why. But a lot of this speculation narrows too quickly, to just one or two hypotheses. My intent is also to speculate, but more broadly.
Specifically, I want to outline six nonexclusive possible causes for the weird tokens: new better language, spandrels, context refresh, deliberate obfuscation, natural drift, and conflicting shards.
And I also wish to extremely roughly outline ideas for experiments and evidence that could help us distinguish these causes.
I'm sure I'm not enumerating the full space of [...]
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Outline:(00:11) Intro
(01:34) 1. New Better Language
(04:06) 2. Spandrels
(06:42) 3. Context Refresh
(10:48) 4. Deliberate Obfuscation
(12:36) 5. Natural Drift
(13:42) 6. Conflicting Shards
(15:24) Conclusion
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First published: October 9th, 2025
Source: https://www.lesswrong.com/posts/qgvSMwRrdqoDMJJnD/towards-a-typology-of-strange-llm-chains-of-thought ---
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