StateX: Enhancing RNN Recall via Post-training State Expansion

While Transformer-based models have demonstrated remarkable language modeling performance, their high complexities result in high costs when processing long contexts. In contrast, recurrent neural networks (RNNs) such as linear attention and state space models have gained popularity due to their constant per-token complexities. However, these recurrent models struggle with tasks that require accurate recall of contextual information from long contexts, because all contextual information is compressed into a constant-size recurrent state. Previous works have shown that recall ability is positively correlated with the recurrent state size, yet directly training RNNs with larger recurrent states results in high training costs. In this paper, we introduce StateX, a training pipeline for efficiently expanding the states of pre-trained RNNs through post-training. For two popular classes of RNNs, linear attention and state space models, we design post-training architectural modifications to scale up the state size with no or negligible increase in model parameters. Experiments on models up to 1.3B parameters demonstrate that StateX efficiently enhances the recall and in-context learning ability of RNNs without incurring high post-training costs or compromising other capabilities. Recently, recurrent neural networks (RNNs) such as gated linear attention (GLA) (Y ang et al., 2024) and Mamba2 (Dao & Gu, 2024) have shown promising capabilities in language modeling. These architectures have constant per-token complexity, while the more popular Transformer architecture (V aswani et al., 2023) has per-token complexity that grows linearly with the context length.