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.