Diffusion-Based Symbolic Regression

ICLR 2026 Conference Submission13587 Authors

Keywords: symbolic regression, diffusion, generative modeling, generative reinforcement learning

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

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.

Primary Area: interpretability and explainable AI

Submission Number: 13587