Gating controls memorization and learning in state space models
On the Importance of Gating: Memorization vs. In-Context Learning in State Space Models
Machine LearningArtificial Intelligence
Summary
State Space Models (SSMs) are an efficient way to process sequences but struggle with learning from context and recalling specific information compared to Transformers. The authors found that a component called gating makes these models focus first on memorizing information inside their parameters and delays or blocks their ability to learn from the sequence in real time. Interestingly, gating also helps these models work better when dealing with longer sequences. This research helps explain how gating shapes learning and could guide improvements in fast, memory-efficient sequence models.
What this means in practice
- •For machine learning engineers: Improve sequence models for language tasks by adjusting gating to balance memorization and in-context learning.
- •For natural language processing teams: Design efficient language models that handle long texts better by leveraging gating mechanisms in state space models.
Authors
William L. Tong, Aryo Lotfi, Emmanuel Abbe, Kostas Vaggelakos, Vishnu Banna, Etai Littwin, Josh Susskind, Cengiz Pehlevan, Eran Malach
Abstract
State Space Models (SSMs) have emerged as a compelling alternative to Transformers, enabling sequence modeling with constant memory and linear compute. Although SSMs exhibit reasonable performance and favorable computational characteristics, they continue to lag behind Transformers on tasks that require in-context learning and precise retrieval, slowing their adoption for large-scale language modeling. In this work, we demonstrate that both the success and failure of SSMs in these domains can be explained by studying the role of the gating mechanism, a prevalent component in modern recurrent networks. Specifically, we show through theory and experiments that this gating mechanism causes SSMs to first learn an in-weights "memorization" solution, while delaying, or even preventing, convergence to a correct in-context learning solution. Importantly, this happens even in cases where there are no fundamental limitations due to the architecture or its memory capacity. On the other hand, we find that gating is often beneficial for improving generalization to long sequence lengths. Our results illuminate the crucial role of the gating mechanism in shaping both the training dynamics and generalization of SSMs, and provide a basis for understanding and improving linear-time models.