# Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning

arXiv: https://arxiv.org/abs/2501.01085 · HTML: https://mlsanity.com/paper/2501.01085/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Interpretable and logic-guided RL, one of 15 directions in Deep reinforcement learning methods.
- First posted on arXiv 2 Jan 2025.

## Placed in

- Deep reinforcement learning methods / [Interpretable and logic-guided RL](https://mlsanity.com/deeprl/interpretable-and-logic-guided-rl/)

## Built on by (most cited first, 6 of 6)

- [Think Hierarchically, Act Dynamically: Hierarchical Multi-modal Fusion and Reasoning for Vision-and-Language Navigation](https://mlsanity.com/paper/2504.16516/) (2025)
- [Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression](https://mlsanity.com/paper/2506.08267/) (2025)
- [FePySR: A Neural Feature Extraction Framework for Efficient and Scalable Symbolic Regression](https://mlsanity.com/paper/2605.12704/) (2026)
- [SymMatika: Structure-Aware Symbolic Discovery](https://mlsanity.com/paper/2507.03110/) (2025)
- [Complex Instruction Following with Diverse Style Policies in Football Games](https://mlsanity.com/paper/2511.19885/) (2025)
- [SMILE: Bridging Continuous Optimization and Discrete Symbolic Recovery](https://mlsanity.com/paper/2609.04639/) (2026)

## Abstract (arXiv)

Symbolic regression (SR) has emerged as a pivotal technique for uncovering the intrinsic information within data and enhancing the interpretability of AI models. However, current state-of-the-art (sota) SR methods struggle to perform correct recovery of symbolic expressions from high-noise data. To address this issue, we introduce a novel noise-resilient SR (NRSR) method capable of recovering expressions from high-noise data. Our method leverages a novel reinforcement learning (RL) approach in conjunction with a designed noise-resilient gating module (NGM) to learn symbolic selection policies. The gating module can dynamically filter the meaningless information from high-noise data, thereby demonstrating a high noise-resilient capability for the SR process. And we also design a mixed path entropy (MPE) bonus term in the RL process to increase the exploration capabilities of the policy. Experimental results demonstrate that our method significantly outperforms several popular baselines on benchmarks with high-noise data. Furthermore, our method also can achieve sota performance on benchmarks with clean data, showcasing its robustness and efficacy in SR tasks.