Machine Studying | Jacob Xiaochen Li

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We increasingly need AI agents to work in domains they never saw during training, like using a new programming library or leveraging the emerging literature around a new disease. Such domains most naturally appear as a corpus of documents, like a textbook on a technical subject or the manual describing a new tool. Faced with such a corpus, current agents overwhelmingly rely on inference compute and immediately reduce this problem either to “RAG” or to “long context”, and then simply rely on in-context learning, on weight updates that approximate it, or on agentic search and recursion that scales it to longer contexts. If a domain is important enough, today’s best practice is to hand-build an RL environment (or buy one!) so agents can practice some relevant skills via trial and error. Across all of these, we can’t help but notice that our agents today engage with new domains in shallow, hand-engineered ways. Humans can turn reading a textbook and actively thinking about the material int

Machine Studying | Jacob Xiaochen Li Machine Studying We increasingly need AI agents to work in domains they never saw during training, like using a new programming library or leveraging the emerging literature around a new disease. Such domains most naturally appear as a corpus of documents , like a textbook on a technical subject or the manual describing a new tool. Faced with such a corpus, current agents overwhelmingly rely on inference compute and immediately reduce this problem either to “RAG” or to “long context”, and then simply rely on in-context learning, on weight updates that appro

Machine Studying | Jacob Xiaochen Li

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