Title: Towards Efficient Unroll Generalization in Learned Optimizers
Primary area: optimization
Keywords: deep learning, meta-learning, learned optimizers/learned optimization, unroll generalization, optimization, replay buffer, imitation learning, computer vision, language models

Abstract: Recent works have demonstrated that learned optimizers (LOs) can be competitive and sometimes even outperform hand-designed counterparts, highlighting their potential as a pathway toward developing better optimization algorithms. Yet, despite this promise, meta-generalization remains a major challenge for LOs. In particular, they often struggle to maintain stable convergence over long unrolls, as they are typically meta-trained only on short horizons. While extending the unroll length during meta-training may seem like a natural remedy, in practice it substantially increases computational cost (at least linearly) and frequently leads to divergence or collapse due to compounding errors. To improve the long unroll generalization of LOs, we propose a novel meta-training scheme called Efficient Long-horizon Learning (ELO), which leverages a replay buffer to efficiently extend unroll length during meta-training without adding extra meta-training cost. In addition, it integrates online behavior cloning to stabilize meta-training and potentially inherit the generalization benefits of hand-designed optimizers. We evaluate ELO on a variety of vision and language tasks, showing its success in achieving long-unroll generalization in practical scenarios.