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METTA: Memory-Enhanced Test-Time Adaptation for Meta-Reinforcement Learning

Ali Najar, Hosein Hasani, Amir Kooshan Fattah, Yasamin Rajabi, Mahdieh Soleymani Baghshah · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31656 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

メタパッケージの学習は、限られた相互作用から見えないタスクに迅速に適応するエージェントを訓練することを目指していますが、これは長い相互作用の歴史を代表するタスク証拠を必要とします。クラシックの繰り返しエージェントは、効率的な状態にエクスペリエンスを圧縮し、トランスフォーマーベースのエージェントは、過去の軌道から四角形のメモリおよび計算コストで回収します。この作品では、メモリ拡張テストタイムアダプタ(METTA)をメタパッケージの歴史を参照することなく、メモリ拡張テストタイムアダプタを提案します。さらに、スピードアダプタを2つの時間スケールに分解します

Machine translation (Meta M2M100); the English original follows.

Abstract

Meta-reinforcement learning aims to train agents that adapt quickly to unseen tasks from limited interaction, but this requires representing task evidence over long interaction histories. Classical recurrent agents compress experience into hidden states, while transformer-based agents retrieve from past trajectories at quadratic memory and computation cost. In this work, we propose Memory-Enhanced Test-Time Adaptation (METTA) for meta-reinforcement learning. It decomposes the fast adaptation phase into two temporal scales: a causal neural memory performs fine-grained within-episode adaptation with linear sequence complexity, while compact episode-level summaries support long-range cross-episode task inference without attending over the full interaction history. Moreover, equipping agents with neural memory provides sufficient expressive capacity and test-time training capability, enabling effective adaptation to unseen held-out tasks and task families at meta-test time. On unseen held-out Meta-World ML10/45 test environments, METTA achieves a success rate over $50\%$ while maintaining a low training budget and short wall-clock time. On long-context sparse-reward PointMaze tasks, it outperforms transformer-based baselines by exhibiting more structured exploration behavior. These results show that METTA provides an efficient path toward more adaptive and scalable long-context meta-RL.

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Topics: Computer science, Task (project management), Adaptation (eye), Artificial intelligence, Scalability, Machine learning

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