Were RNNs All We Needed?
Keywords: Recurrent Neural Networks, RNNs, Sequence Modelling, Efficiency, LSTMs, GRUs, Parallel Scan
TL;DR: Revisiting decade-old RNNs (LSTMs and GRUs), we introduce minimal versions (minLSTMs and minGRUs) that (1) use fewer parameters, (2) are parallelizable during training, and (3) match the performance of recent sequence models.
Abstract: The scalability limitations of Transformers regarding sequence length have renewed interest in recurrent sequence models that are parallelizable during training. As a result, many novel recurrent architectures, such as S4, Mamba, and Aaren, have been proposed that achieve comparable performance. In this work, we revisit traditional recurrent neural networks (RNNs) from over a decade ago: LSTMs (1997) and GRUs (2014). While these models were slow due to requiring to backpropagate through time (BPTT), we show that by removing their hidden state dependencies from their input, forget, and update gates, LSTMs and GRUs no longer need to BPTT and can be efficiently trained in parallel. Building on this, we introduce minimal versions (minLSTMs and minGRUs) that (1) use significantly fewer parameters than their traditional counterparts and (2) are fully parallelizable during training ($175 \times$ faster for a sequence of length $512$). Lastly, we show that these stripped-down versions of decade-old RNNs match the empirical performance of recent sequence models.
Primary Area: foundation or frontier models, including LLMs
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Submission Number: 8241
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