TLDR: Most AI systems get better at tasks without getting any better at learning. DeepMind's DiscoRL goes after that machinery itself: a neural network represents the update rule, and a population of agents supplies the experience used to discover rules that produce better learners. Its Disco57 rule, discovered on Atari57, trained fresh agents on ProcGen and DMLab-30 without further meta-training, a concrete case of intelligence being applied to the procedure that creates intelligence. We trace the lineage from MAML, learned optimizers, and Learned Policy Gradient, then unpack the two-level credit-assignment problem inside DiscoRL's loop, what zero-shot transfer of a learning rule really tests, why the economics come down to amortizing discovery cost across many training runs, and why interpretability matters before anyone adopts a learned rule. We close with what a genuinely recursive next step would have to show: discovered improvements that keep producing measurable gains after...
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