Title: Diffusion-Based Symbolic Regression
Primary area: interpretability and explainable AI
Keywords: symbolic regression, diffusion, generative modeling, generative reinforcement learning

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.

TL;DR: Discrete diffusion-based approach for symbolic regression in a reinforcement learning framework.