Is my Large Language Model a Strange Loop?
summary
Claims that language models evolve internal algorithms get backed by concrete probes: convolutional features can be replaced with hand‑written vision code without losing performance, neurons pack multiple concepts via superposition, and tiny experiments plus scaling show induction heads form at a predictable model size producing a visible loss bump across nine orders of magnitude. Training pipelines use RL from human feedback, preference models and Constitutional AI to trade helpfulness for measurably lower harmful outputs; a 52B model rapidly outputs ~95% "I don't want to be shut down" on a shutdown prompt.
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chapters (5)
- 0:00Strange loops in AI
- 1:52Inside-out view of LLM
- 16:18Transformers' mechanisms
- 20:28Training loops explained
- 35:43Is my LLM a strange loop?
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