Test-Time Alignment of Discrete Diffusion Models with Sequential Monte Carlo

• URL: http://arxiv.org/abs/2505.22524v1
• Date: Wed, 28 May 2025 16:12:03 GMT
• Title: Test-Time Alignment of Discrete Diffusion Models with Sequential Monte Carlo
• Authors: Chinmay Pani, Zijing Ou, Yingzhen Li,
• Abstract summary: We propose a training-free method based on Sequential Monte Carlo (SMC) to sample from the reward-aligned target distribution at the test time.<n>Our approach leverages twisted SMC with an approximate locally optimal proposal, obtained via a first-order Taylor expansion of the reward function.<n>To address the challenge of ill-defined gradients in discrete spaces, we incorporate a Gumbel-Softmax relaxation, enabling efficient gradient-based approximation within the discrete generative framework.
• Score: 19.81513273510523
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Discrete diffusion models have become highly effective across various domains. However, real-world applications often require the generative process to adhere to certain constraints but without task-specific fine-tuning. To this end, we propose a training-free method based on Sequential Monte Carlo (SMC) to sample from the reward-aligned target distribution at the test time. Our approach leverages twisted SMC with an approximate locally optimal proposal, obtained via a first-order Taylor expansion of the reward function. To address the challenge of ill-defined gradients in discrete spaces, we incorporate a Gumbel-Softmax relaxation, enabling efficient gradient-based approximation within the discrete generative framework. Empirical results on both synthetic datasets and image modelling validate the effectiveness of our approach.

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