Researchers on LessWrong are exploring what they call 'continual learning'—the idea that AI agents persistently improve during deployment, not just at training time. The thinking goes like this: if an AI agent could learn from its own successes and failures the way human researchers do over years of practice, it might make much faster progress on open-ended work like AI research itself. The sequence tackles three core questions: what exactly counts as continual learning, how might it change safety and alignment risks, and what can we do today to make continual-learning agents safer? Early forms already exist—like context windows and memory banks—but the implications if agents get truly good at learning on the job could be significant.

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