Pretraining Recurrent Networks without Recurrence

• URL: http://arxiv.org/abs/2606.06479v1
• Date: Thu, 04 Jun 2026 17:57:33 GMT
• Title: Pretraining Recurrent Networks without Recurrence
• Abstract summary: Training recurrent neural networks (RNNs) requires assigning credit across long sequences of computations.<n>We propose Supervised Memory Training (SMT), a method for training nonlinear RNNs that sidesteps recurrent credit propagation entirely.
• Score: 28.905819079755762
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training recurrent neural networks (RNNs) requires assigning credit across long sequences of computations. Standard backpropagation through time (BPTT) addresses this problem poorly: it is sequential in time, limiting parallelism, and suffers from vanishing or exploding gradients, making long-range associations difficult to learn. We propose Supervised Memory Training (SMT), a method for training nonlinear RNNs that sidesteps recurrent credit propagation entirely by reducing RNN training to supervised learning on one-step memory transition labels $(m_t, x_{t+1}) \rightarrow m_{t+1}$. SMT acquires these memory labels by training a Transformer-based encoder on a predictive state objective--retaining only information from the past necessary to predict the future. By decoupling what to remember from how to update memory, SMT enables time-parallel RNN training with a stable $O(1)$ length gradient path between any two tokens--without ever unrolling the RNN. We find that SMT outperforms BPTT when pretraining various RNN architectures on tasks like language modeling and pixel sequence modeling. SMT enables nonlinear RNNs to better capture long-range dependencies and train in parallel, potentially unlocking the scaling of models that build temporal abstractions of past experience.

Related papers

• Efficient Sparse Selective-Update RNNs for Long-Range Sequence Modeling [16.27516369166629]
  Selective-Update RNNs (suRNNs) are a non-linear architecture that learns to preserve its memory when the input is redundant.<n>By using a neuron-level binary switch that only opens for informative events, suRNNs decouple the recurrent updates from the raw sequence length.<n>Experiments on the Long Range Arena, WikiText, and other synthetic benchmarks show that suRNNs match or exceed the accuracy of much more complex models.
  arXiv  Detail & Related papers  (2026-02-11T14:50:07Z)
• Parallelizable memory recurrent units [1.3159512679346688]
  We introduce a new family of RNNs, the memory recurrent units (MRUs), that combine the persistent memory capabilities of nonlinear RNNs with the parallelizable computations of SSMs.<n>We show that BMRU achieves good results in tasks with long-term dependencies, and can be combined with state-space models to create hybrid networks that are parallelizable and have transient dynamics as well as persistent memory.
  arXiv  Detail & Related papers  (2026-01-14T14:01:11Z)
• MesaNet: Sequence Modeling by Locally Optimal Test-Time Training [67.45211108321203]
  We introduce a numerically stable, chunkwise parallelizable version of the recently proposed Mesa layer.<n>We show that optimal test-time training enables reaching lower language modeling perplexity and higher downstream benchmark performance than previous RNNs.
  arXiv  Detail & Related papers  (2025-06-05T16:50:23Z)
• Scaling Recurrent Neural Networks to a Billion Parameters with Zero-Order Optimization [0.0]
  RNNs scale constant in FLOPs and GPU memory with increasing context length.<n>Transformers scale linearly in FLOPs and, at best, linearly in memory during generation.<n>Training large RNNs on long contexts remains impractical because standard optimization methods depend on Backpropagation Through Time (BPTT)
  arXiv  Detail & Related papers  (2025-05-23T13:04:06Z)
• Optimal Gradient Checkpointing for Sparse and Recurrent Architectures using Off-Chip Memory [0.8321953606016751]
  We introduce memory-efficient gradient checkpointing strategies tailored for the general class of sparse RNNs and Spiking Neural Networks.<n>We find that Double Checkpointing emerges as the most effective method, optimizing the use of local memory resources while minimizing recomputation overhead.
  arXiv  Detail & Related papers  (2024-12-16T14:23:31Z)
• Attention as an RNN [66.5420926480473]
  We show that attention can be viewed as a special Recurrent Neural Network (RNN) with the ability to compute its textitmany-to-one RNN output efficiently. We introduce a new efficient method of computing attention's textitmany-to-many RNN output based on the parallel prefix scan algorithm. We show Aarens achieve comparable performance to Transformers on $38$ datasets spread across four popular sequential problem settings.
  arXiv  Detail & Related papers  (2024-05-22T19:45:01Z)
• Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks [70.75043144299168]
  Spiking Neural Networks (SNNs) are promising energy-efficient models for neuromorphic computing. We propose the Spatial Learning Through Time (SLTT) method that can achieve high performance while greatly improving training efficiency. Our method achieves state-of-the-art accuracy on ImageNet, while the memory cost and training time are reduced by more than 70% and 50%, respectively, compared with BPTT.
  arXiv  Detail & Related papers  (2023-02-28T05:01:01Z)
• Intelligence Processing Units Accelerate Neuromorphic Learning [52.952192990802345]
  Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency. We present an IPU-optimized release of our custom SNN Python package, snnTorch.
  arXiv  Detail & Related papers  (2022-11-19T15:44:08Z)
• Online Training Through Time for Spiking Neural Networks [66.7744060103562]
  Spiking neural networks (SNNs) are promising brain-inspired energy-efficient models. Recent progress in training methods has enabled successful deep SNNs on large-scale tasks with low latency. We propose online training through time (OTTT) for SNNs, which is derived from BPTT to enable forward-in-time learning.
  arXiv  Detail & Related papers  (2022-10-09T07:47:56Z)
• Tensor train decompositions on recurrent networks [60.334946204107446]
  Matrix product state (MPS) tensor trains have more attractive features than MPOs, in terms of storage reduction and computing time at inference. We show that MPS tensor trains should be at the forefront of LSTM network compression through a theoretical analysis and practical experiments on NLP task.
  arXiv  Detail & Related papers  (2020-06-09T18:25:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.