Solving Bayesian inverse problems with diffusion priors and off-policy RL

• URL: http://arxiv.org/abs/2503.09746v1
• Date: Wed, 12 Mar 2025 18:45:22 GMT
• Title: Solving Bayesian inverse problems with diffusion priors and off-policy RL
• Authors: Luca Scimeca, Siddarth Venkatraman, Moksh Jain, Minsu Kim, Marcin Sendera, Mohsin Hasan, Luke Rowe, Sarthak Mittal, Pablo Lemos, Emmanuel Bengio, Alexandre Adam, Jarrid Rector-Brooks, Yashar Hezaveh, Laurence Perreault-Levasseur, Yoshua Bengio, Glen Berseth, Nikolay Malkin,
• Abstract summary: Relative Trajectory Balance (RTB) is an off-policy reinforcement learning objective that canally solve inverse problems optimally.<n>We extend the original work by using RTB to train conditional diffusion model posteriors from pretrained unconditional priors for challenging linear and non-linear inverse problems in vision, and science.
• Score: 86.65351676007721
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a practical application of Relative Trajectory Balance (RTB), a recently introduced off-policy reinforcement learning (RL) objective that can asymptotically solve Bayesian inverse problems optimally. We extend the original work by using RTB to train conditional diffusion model posteriors from pretrained unconditional priors for challenging linear and non-linear inverse problems in vision, and science. We use the objective alongside techniques such as off-policy backtracking exploration to improve training. Importantly, our results show that existing training-free diffusion posterior methods struggle to perform effective posterior inference in latent space due to inherent biases.

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