ArXiv ID: 2603.03524
Summary: MASS is a meta-learning framework that enables LLMs to self-adapt by generating problem-specific synthetic training data and performing targeted self-updates at inference time. Trained via bilevel optimization, an inner loop adapts on self-generated examples while an outer loop meta-learns data-attribution signals. Experiments on mathematical reasoning show effective per-instance curricula.