3 papers
cs.LG2026
Reinforce Adjoint Matching: Scaling RL Post-Training of Diffusion and Flow-Matching Models
Andreas Bergmeister, Stefanie Jegelka, Nikolas Nüsken +2
Diffusion and flow-matching models scale because pretraining is supervised regression: a clean sample is noised analytically, and a model regresses against a closed-form target. RL…
cs.LG2026
A projection-based framework for gradient-free and parallel learning
Andreas Bergmeister, Manish Krishan Lal, Stefanie Jegelka +1
We present a feasibility-seeking approach to neural network training. This mathematical optimization framework is distinct from conventional gradient-based loss minimization and us…
physics.geo-ph2025
High Resolution Seismic Waveform Generation using Denoising Diffusion
Kadek Hendrawan Palgunadi, Andreas Bergmeister, Andrea Bosisio +5
Accurate prediction and synthesis of seismic waveforms are crucial for seismic-hazard assessment and earthquake-resistant infrastructure design. Existing prediction methods, such a…