SMT解决了RNN训练中并行性差和长程依赖难学的问题，做序列建模或时间序列分析的开发者可以直接用这个方法替代BPTT，训练效率会大幅提升。
论文提出Supervised Memory Training (SMT)，一种训练非线性RNN的新方法。SMT通过将RNN训练转化为一步记忆转换标签的监督学习，完全绕过了传统的循环信用分配。它利用基于Transformer的编码器从预测状态目标中获取记忆标签，只保留预测未来所需的过去信息。SMT实现了时间并行训练，任意两个token之间的梯度路径长度稳定为O(1)，无需展开RNN。实验表明，SMT在语言建模和像素序列建模等任务上优于BPTT，能更好地捕捉长程依赖关系。
Pretraining Recurrent Networks without Recurrence
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.