Diffusion-Based Symbolic Regression

• URL: http://arxiv.org/abs/2505.24776v1
• Date: Fri, 30 May 2025 16:39:29 GMT
• Title: Diffusion-Based Symbolic Regression
• Authors: Zachary Bastiani, Robert M. Kirby, Jacob Hochhalter, Shandian Zhe,
• Abstract summary: Diffusion has emerged as a powerful framework for generative modeling, achieving remarkable success in applications such as image and audio synthesis.<n>We propose a novel diffusion-based approach for symbolic regression.<n>We construct a random mask-based diffusion and denoising process to generate diverse and high-quality equations.
• Score: 20.941908494137806
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Diffusion has emerged as a powerful framework for generative modeling, achieving remarkable success in applications such as image and audio synthesis. Enlightened by this progress, we propose a novel diffusion-based approach for symbolic regression. We construct a random mask-based diffusion and denoising process to generate diverse and high-quality equations. We integrate this generative processes with a token-wise Group Relative Policy Optimization (GRPO) method to conduct efficient reinforcement learning on the given measurement dataset. In addition, we introduce a long short-term risk-seeking policy to expand the pool of top-performing candidates, further enhancing performance. Extensive experiments and ablation studies have demonstrated the effectiveness of our approach.

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