Tuning the burn-in phase in training recurrent neural networks improves their performance

2026-02-11

AI Summary

Standard backpropagation through time (BPTT) incurs substantial computational costs in long-sequence tasks, while truncated BPTT improves efficiency at the expense of performance degradation. This work establishes, for the first time, a theoretical error bound characterizing the performance loss induced by truncated BPTT, revealing that the burn-in phase is a critical hyperparameter governing model performance. Building on this insight, the authors propose a targeted tuning strategy for the burn-in duration. Experiments on system identification and time series prediction benchmarks demonstrate that appropriately configuring the burn-in phase can reduce both training and test prediction errors by over 60%, offering a novel perspective for achieving both efficiency and high performance in recurrent neural network training.

Technology Category

• Natural Language Processing: Learning & Optimization for NLP
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Training recurrent neural networks (RNNs) with standard backpropagation through time (BPTT) can be challenging, especially in the presence of long input sequences. A practical alternative to reduce computational and memory overhead is to perform BPTT repeatedly over shorter segments of the training data set, corresponding to truncated BPTT. In this paper, we examine the training of RNNs when using such a truncated learning approach for time series tasks. Specifically, we establish theoretical bounds on the accuracy and performance loss when optimizing over subsequences instead of the full data sequence. This reveals that the burn-in phase of the RNN is an important tuning knob in its training, with significant impact on the performance guarantees. We validate our theoretical results through experiments on standard benchmarks from the fields of system identification and time series forecasting. In all experiments, we observe a strong influence of the burn-in phase on the training process, and proper tuning can lead to a reduction of the prediction error on the training and test data of more than 60% in some cases.

Problem

• recurrent neural networks
• truncated BPTT
• burn-in phase
• performance loss
• time series forecasting

Innovation

• burn-in phase
• truncated BPTT
• recurrent neural networks
• theoretical bounds
• time series forecasting