Diffusion-Based Symbolic Regression
This is an incremental improvement for symbolic regression, applying diffusion models to a new domain.
The authors tackled symbolic regression by proposing a diffusion-based approach that generates equations through a random mask-based diffusion and denoising process, integrated with reinforcement learning, achieving high-quality results as demonstrated in extensive experiments.
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.