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Martin Casado recommends an MIT talk for an intuitive grasp of LLMs
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1 min read
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Vishal Misra’s MIT talk explains how SFT, RLHF and RL reshape a distribution while next-token prediction remains underneath; Martin Casado recommends it for understanding in-context learning and LLMs.
MIT talk on LLMs (video; descriptive title)
Resource/creator: Vishal Misra’s MIT talk on LLMs. Recommended by: Martin Casado, who calls it the best talk on in-context learning and says it builds an intuitive grasp of LLMs.
Key takeaway: Misra describes a first-principles account that skips attention and transformers: SFT/RLHF/R reshape the distribution, while the underlying LLM remains a next-token predictor. Why it matters: It connects post-training methods to the model’s underlying prediction process—the intuition Casado specifically recommends the talk for.