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What's so hard about continuous learning?

seangoedecke.com · 1,687 words · saved by 2 readers

Why can’t models continue to get smarter after they’re deployed? If you hire a human employee, they will grow more familiar with your systems over time, and (if they stick around long enough) eventually become a genuine domain expert. AI models are not like this. They are always exactly as capable as the first moment you use them. This is because model weights are frozen once the model is released. The model can only “learn” as much as can be stuffed into its context window: in effect, it can take new information into its short-term working memory, but not its long-term memory. “Continuous learning” - the ability for a model to update its own weights over time - is thus often described as the bottleneck for AGI1. However, the mechanics of continuous learning are not hard. The technical problem of “how do you change the weights of a model at runtime” is straightforward. It’s the exact same process as post-training: you simply keep running new user input through the training pipeline you

Why can’t models continue to get smarter after they’re deployed? If you hire a human employee, they will grow more familiar with your systems over time, and (if they stick around long enough) eventually become a genuine domain expert. AI models are not like this. They are always exactly as capable as the first moment you use them. This is because model weights are frozen once the model is released. The model can only “learn” as much as can be stuffed into its context window: in effect, it can take new information into its short-term working memory, but not its long-term memory. “Continuous lea

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