# MILES: Modality-Informed Learning Rate Scheduler for Balancing Multimodal Learning

arXiv: https://arxiv.org/abs/2510.17394 · HTML: https://mlsanity.com/paper/2510.17394/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 26 references that are in the map.
- First posted on arXiv 20 Oct 2025.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Multimodal Learning with Transformers: A Survey](https://mlsanity.com/paper/2206.06488/) (2022)
- [What Makes Training Multi-Modal Classification Networks Hard?](https://mlsanity.com/paper/1905.12681/) (2019)
- [Balanced Multimodal Learning via On-the-fly Gradient Modulation](https://mlsanity.com/paper/2203.15332/) (2022)
- [Gated Multimodal Units for Information Fusion](https://mlsanity.com/paper/1702.01992/) (2017)
- [Modality Competition: What Makes Joint Training of Multi-modal Network Fail in Deep Learning? (Provably)](https://mlsanity.com/paper/2203.12221/) (2022)
- [Characterizing and overcoming the greedy nature of learning in multi-modal deep neural networks](https://mlsanity.com/paper/2202.05306/) (2022)
- [LUMA: A Benchmark Dataset for Learning from Uncertain and Multimodal Data](https://mlsanity.com/paper/2406.09864/) (2024)
- [MIND: Modality-Informed Knowledge Distillation Framework for Multimodal Clinical Prediction Tasks](https://mlsanity.com/paper/2502.01158/) (2025)

## Abstract (arXiv)

The aim of multimodal neural networks is to combine diverse data sources, referred to as modalities, to achieve enhanced performance compared to relying on a single modality. However, training of multimodal networks is typically hindered by modality overfitting, where the network relies excessively on one of the available modalities. This often yields sub-optimal performance, hindering the potential of multimodal learning and resulting in marginal improvements relative to unimodal models. In this work, we present the Modality-Informed Learning ratE Scheduler (MILES) for training multimodal joint fusion models in a balanced manner. MILES leverages the differences in modality-wise conditional utilization rates during training to effectively balance multimodal learning. The learning rate is dynamically adjusted during training to balance the speed of learning from each modality by the multimodal model, aiming for enhanced performance in both multimodal and unimodal predictions. We extensively evaluate MILES on four multimodal joint fusion tasks and compare its performance to seven state-of-the-art baselines. Our results show that MILES outperforms all baselines across all tasks and fusion methods considered in our study, effectively balancing modality usage during training. This results in improved multimodal performance and stronger modality encoders, which can be leveraged when dealing with unimodal samples or absent modalities. Overall, our work highlights the impact of balancing multimodal learning on improving model performance.