arXiv:2607.06772v1 Announce Type: new Abstract: Learned optimization aims to improve upon hand-designed optimizers (e.g., Adam and Muon) by meta-learning small neural network optimizers over a distribution of tasks. While recent work has greatly advanced the architectural design and inductive biases of learned optimizers (LOs), current meta-training approaches still suffer from two main difficulties: (1) they cannot efficiently scale meta-training to long-horizon inner problems and (2) they often fail to surpass comparable hand-designed optimizers. To address these limitations, we propose Effi
Source: arXiv cs.LG — read the full report at the original publisher.