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Why Richard Sutton thinks LLMs go against the bitter lesson:

Dwarkesh Patel

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67,497 次观看 • 10 个月前 •via X (Twitter)

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Lots of people think it's obvious that humans learn by imitation, starting with babies imitating their parents. Richard Sutton thinks this is completely wrong. Sutton basically thinks direct imitation plays almost no role in animal learning, and at most a small role in human intelligence. When one animal ends up doing the same thing as another, he thinks this is the result of RL-style trial-and-error, not direct imitation. And if imitation isn't really a kind of learning, then training LLMs to imitate humans isn't really Bitter Lesson–pilled.

Lots of people think it's obvious that humans learn by imitation, starting with babies imitating their parents. Richard Sutton thinks this is completely wrong. Sutton basically thinks direct imitation plays almost no role in animal learning, and at most a small role in human intelligence. When one animal ends up doing the same thing as another, he thinks this is the result of RL-style trial-and-error, not direct imitation. And if imitation isn't really a kind of learning, then training LLMs to imitate humans isn't really Bitter Lesson–pilled.

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.Richard Sutton, father of reinforcement learning, doesn’t think LLMs are bitter-lesson-pilled. My steel man of Richard’s position: we need some new architecture to enable continual (on-the-job) learning. And if we have continual learning, we don't need a special training phase - the agent just learns on-the-fly - like all humans, and indeed, like all animals. This new paradigm will render our current approach with LLMs obsolete. I did my best to represent the view that LLMs will function as the foundation on which this experiential learning can happen. Some sparks flew. 0:00:00 – Are LLMs a dead-end? 0:13:51 – Do humans do imitation learning? 0:23:57 – The Era of Experience 0:34:25 – Current architectures generalize poorly out of distribution 0:42:17 – Surprises in the AI field 0:47:28 – Will The Bitter Lesson still apply after AGI? 0:54:35 – Succession to AI

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