Implicit Language Models are RNNs: Balancing Parallelization and Expressivity
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915338607656960 |
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| author | Schöne, Mark Rahmani, Babak Kremer, Heiner Falck, Fabian Ballani, Hitesh Gladrow, Jannes |
| author_facet | Schöne, Mark Rahmani, Babak Kremer, Heiner Falck, Fabian Ballani, Hitesh Gladrow, Jannes |
| contents | State-space models (SSMs) and transformers dominate the language modeling landscape. However, they are constrained to a lower computational complexity than classical recurrent neural networks (RNNs), limiting their expressivity. In contrast, RNNs lack parallelization during training, raising fundamental questions about the trade off between parallelization and expressivity. We propose implicit SSMs, which iterate a transformation until convergence to a fixed point. Theoretically, we show that implicit SSMs implement the non-linear state-transitions of RNNs. Empirically, we find that only approximate fixed-point convergence suffices, enabling the design of a scalable training curriculum that largely retains parallelization, with full convergence required only for a small subset of tokens. Our approach demonstrates superior state-tracking capabilities on regular languages, surpassing transformers and SSMs. We further scale implicit SSMs to natural language reasoning tasks and pretraining of large-scale language models up to 1.3B parameters on 207B tokens representing, to our knowledge, the largest implicit model trained to date. Notably, our implicit models outperform their explicit counterparts on standard benchmarks. Our code is publicly available at http://github.com/microsoft/implicit_languagemodels . |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_07827 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Implicit Language Models are RNNs: Balancing Parallelization and Expressivity Schöne, Mark Rahmani, Babak Kremer, Heiner Falck, Fabian Ballani, Hitesh Gladrow, Jannes Machine Learning Artificial Intelligence State-space models (SSMs) and transformers dominate the language modeling landscape. However, they are constrained to a lower computational complexity than classical recurrent neural networks (RNNs), limiting their expressivity. In contrast, RNNs lack parallelization during training, raising fundamental questions about the trade off between parallelization and expressivity. We propose implicit SSMs, which iterate a transformation until convergence to a fixed point. Theoretically, we show that implicit SSMs implement the non-linear state-transitions of RNNs. Empirically, we find that only approximate fixed-point convergence suffices, enabling the design of a scalable training curriculum that largely retains parallelization, with full convergence required only for a small subset of tokens. Our approach demonstrates superior state-tracking capabilities on regular languages, surpassing transformers and SSMs. We further scale implicit SSMs to natural language reasoning tasks and pretraining of large-scale language models up to 1.3B parameters on 207B tokens representing, to our knowledge, the largest implicit model trained to date. Notably, our implicit models outperform their explicit counterparts on standard benchmarks. Our code is publicly available at http://github.com/microsoft/implicit_languagemodels . |
| title | Implicit Language Models are RNNs: Balancing Parallelization and Expressivity |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2502.07827 |