Future agents will keep refactoring memory, building RL environments, and distilling to old checkpoints
manosaie · x · 2026-10-15
willcb sketches how autonomous agents will operate: constantly refactoring their own memory, constructing RL environments that match their deployment settings, training continuously, incorporating new world knowledge via synthetic on-policy traces, and periodically distilling into earlier checkpoints to reset plasticity.
The picture moves agents from static models plus prompting toward a deploy-and-train loop — a continuous learning lifecycle.
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