PyTorch Conference Europe 2026

Jacobian descent (JD) is an extension of gradient descent supporting the optimization of vector-valued functions. This algorithm can be used to train neural networks with multiple loss functions (e.g. multi-task learning). JD iteratively updates the parameters of the model using the Jacobian matrix of the vector of losses (the matrix stacking each individual loss' gradient).

To support and extend our research, we have developed the TorchJD library. With it, it's easy and efficient to compute the Jacobians with respect to the model parameters, and to aggregate them into an update direction that is beneficial to every objective. In contrast, if we had averaged the losses and used gradient descent, the update would have been beneficial to the average loss, but may have actually increased one of the individual losses.

In this session, we will give a quick introduction to the theory behind Jacobian descent, and then show how to use TorchJD on a variety of use-cases, beyond multi-task learning.

Library: https://github.com/TorchJD/torchjd
Paper: https://arxiv.org/abs/2406.16232