4 citations · 4 across the 4 of their papers we have counts for
3 papers · 1 filter
Kastor: An efficient fine-tuning strategy for generative emulation of PDE simulations
Guillaume Couairon, Alexis Jacq, Yu-Han Wu +4
Machine learning offers a promising avenue to accelerate physical simulations by replacing computationally expensive traditional Partial Differential Equation (PDE) solvers with fa…
Diffusion Fine-tuning with Rewarded Moment Matching Distillation
Alexis Jacq, Guillaume Couairon, Valentin De Bortoli +3
Distillation and Reinforcement Learning (RL) fine-tuning are the primary pillars of diffusion post-training. While traditionally studied in isolation, the interaction between these…
Lazy-MDPs: Towards Interpretable Reinforcement Learning by Learning When to Act
Alexis Jacq, Johan Ferret, Olivier Pietquin +1
Traditionally, Reinforcement Learning (RL) aims at deciding how to act optimally for an artificial agent. We argue that deciding when to act is equally important. As humans, we dri…