Gating Mechanism Found to Bias State Space Models Toward Memorization
Through theory and experiments, this work shows that the gating mechanism common in modern recurrent networks explains why State Space Models lag behind Transformers on in-context learning and precise retrieval: gating drives SSMs to first learn an in-weights memorization solution, delaying or even preventing convergence to a correct in-context learning solution, even absent architectural or memory-capacity limits. Conversely, gating often benefits generalization to long sequence lengths, revealing its dual role in training dynamics.
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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.
- † Harvard University
- ** Work done while at Apple
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