Papers › T-SCEND: Test-time Scalable MCTS-enhanced Diffusion Model

T-SCEND: Test-time Scalable MCTS-enhanced Diffusion Model

4 Feb 2025arXiv:2502.01989archive 2025-07-28

Tao Zhang, Jia-Shu Pan, Ruiqi Feng, Tailin Wu

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. We first show that na\"ively scaling up inference budget for diffusion models yields marginal gain. To address this, the training of T-SCEND consists of a novel linear-regression negative contrastive learning objective to improve the performance-energy consistency of the energy landscape, and a KL regularization to reduce adversarial sampling. During inference, T-SCEND integrates the denoising process with a novel hybrid Monte Carlo Tree Search (hMCTS), which sequentially performs best-of-N random search and MCTS as denoising proceeds. On challenging reasoning tasks of Maze and Sudoku, we demonstrate the effectiveness of T-SCEND's training objective and scalable inference method. In particular, trained with Maze sizes of up to 6×6, our T-SCEND solves 88% of Maze problems with much larger sizes of 15×15, while standard diffusion completely fails.Code to reproduce the experiments can be found at https://github.com/AI4Science-WestlakeU/t_scend.

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