Amortizing intractable inference in diffusion models for vision, language, and control

[2405.20971] Amortizing intractable inference in diffusion models for vision, language, and control Skip to main content Search Submit Donate Log in Computer Science > Machine Learning arXiv:2405.20971 (cs) [Submitted on 31 May 2024 ( v1 ), last revised 28 Aug 2026 (this version, v3)] Title: Amortizing intractable inference in diffusion models for vision, language, and control Authors: Siddarth Venkatraman , Moksh Jain , Luca Scimeca , Minsu Kim , Marcin Sendera , Mohsin Hasan , Luke Rowe , Sarthak Mittal , Pablo Lemos , Emmanuel Bengio , Alexandre Adam , Jarrid Rector-Brooks , Yoshua Bengio , Glen Berseth , Esmeralda S. Whitammer View PDF HTML (experimental) Abstract: Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors in downstream tasks poses an intractable posterior inference problem. This paper studies amortized sampling of the posterior over data, $\mathbf{x}\sim p^{\rm post}(\mathbf{x})\propto p(\mathbf{x})r(\mathbf{x})$, in a model that consists of a diffusion generative model prior $p(\mathbf{x})$ and a black-box constraint or likelihood function $r(\mathbf{x})$. We state and prove the asymptotic correctness of a data-free learning objective, relative trajectory balance, for training a diffusion model that samples from this posterior, a problem that existing methods solve only approximately or in restricted cases. Relative trajectory balance arises from the generative flow network perspective on diffusion models, which allows the use of deep reinforcement learning techniques to improve mode coverage. Experiments illustrate the broad potential of unbiased inference of arbitrary posteriors under diffusion priors: in vision (classifier guidance), language (infilling under a discrete diffusion LLM), and multimodal data (text-to-image generation). Beyond generative modeling, we apply relative trajectory balance to the problem of continuous control with a score-based behavior prior, achieving state-of-the-art results on benchmarks in offline reinforcement learning. Comments: NeurIPS 2024; code: this https URL Subjects: Machine Learning (cs.LG) ; Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2405.20971 [cs.LG] (or arXiv:2405.20971v3 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2405.20971 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.52202/079017-2422 Focus to learn more DOI(s) linking to related resources Submission history From: Esmeralda Whitammer [ view email ] [v1] Fri, 31 May 2024 16:18:46 UTC (19,156 KB) [v2] Mon, 13 Jan 2025 10:14:27 UTC (19,163 KB) [v3] Fri, 28 Aug 2026 06:53:02 UTC (17,327 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2024-05 Change to browse by: cs cs.CV References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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