Fourth-year Ph.D. student

Baptiste Debes

Speech and Image Processing Department (PSI - ESAT), KU Leuven

My research focuses on value-gradient methods in reinforcement learning: using learned environment models to provide richer training signals than scalar rewards or value targets alone. More broadly, I am interested in decoupled training of neural networks, with the goal of reducing the memory footprint of learning and making training less dependent on end-to-end backpropagation.

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Baptiste Debes

Research Interests

Distributional reinforcement learning

Learning distributions over returns, rather than only expected values, to better represent uncertainty in reinforcement learning problems.

Pointers

  • A Distributional Perspective on Reinforcement Learning, Bellemare, Dabney & Munos arXiv
  • Distributional Value Gradients for Stochastic Environments, Debes & Tuytelaars arXiv

Value-gradient methods

Using learned environment models to estimate gradients of return targets with respect to actions, giving policy learning access to richer local information.

Pointers

  • Learning Continuous Control Policies by Stochastic Value Gradients, Heess et al. arXiv
  • How to Learn a Useful Critic? Model-Based Action-Gradient-Estimator Policy Optimization, D'Oro & Jaśkowski arXiv
  • Distributional Value Gradients for Stochastic Environments, Debes & Tuytelaars arXiv

Decoupled training of neural networks

Training neural-network modules with local or predicted learning signals, aiming to reduce update locking and memory requirements during optimization.

Pointers

  • Decoupled Neural Interfaces using Synthetic Gradients, Jaderberg et al. arXiv
  • Sobolev Training for Neural Networks, Czarnecki et al. arXiv

Selected Publications

Distributional Value Gradients for Stochastic Environments

Baptiste Debes and Tinne Tuytelaars

The Fourteenth International Conference on Learning Representations (ICLR), Rio de Janeiro, Brazil, 2026

arXiv Poster Blog @inproceedings{debes2026distributionalvaluegradients, title = {Distributional Value Gradients for Stochastic Environments}, author = {Debes, Baptiste and Tuytelaars, Tinne}, booktitle = {International Conference on Learning Representations}, year = {2026}, url = {https://arxiv.org/abs/2601.20071}, doi = {10.48550/arXiv.2601.20071} }

Multivariate Distributional Reinforcement Learning Using Sliced Divergences

Baptiste Debes and Tinne Tuytelaars

Proceedings of the 43rd International Conference on Machine Learning (ICML), Seoul, South Korea, 2026

arXiv Poster Blog @inproceedings{debes2026multivariatedistributionalrl, title = {Multivariate Distributional Reinforcement Learning Using Sliced Divergences}, author = {Debes, Baptiste and Tuytelaars, Tinne}, booktitle = {International Conference on Machine Learning}, year = {2026}, url = {https://arxiv.org/abs/2605.31222}, doi = {10.48550/arXiv.2605.31222} }

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