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Chain-of-thought activates latent reasoning branches inside LLMs
Executive TL;DR
Models that think step-by-step explore high-probability latent answer branches. Single-step decoding skips 80% of verifiable reasoning capacity.
Key Takeaways
- Token-by-token scratchpads allow models to route through self-verifying hidden states.
- Inference-time compute scaling is replacing pure parameter count as the main capability driver.
- RLHF rewards coherence but requires structured prompting to unlock multi-step proofs.
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High-signal thinkers
Andrej Karpathy
@karpathy
"Chain-of-thought activates latent reasoning branches inside LLMs"
Lex Fridman
@lexfridman
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