{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/t-scend-test-time-scalable-mcts-enhanced","title":"T-SCEND: Test-time Scalable MCTS-enhanced Diffusion Model","arxiv_id":"2502.01989","date":"2025-02-04","proceeding":null,"authors":["Tao Zhang","Jia-Shu Pan","Ruiqi Feng","Tailin Wu"],"abstract":"We introduce Test-time Scalable MCTS-enhanced Diffusion Model (T-SCEND), a novel framework that significantly improves diffusion model's reasoning capabilities with better energy-based training and scaling up test-time computation. 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