# Curiosity-driven RL for symbolic equation solving

arXiv: https://arxiv.org/abs/2510.17022 · HTML: https://mlsanity.com/paper/2510.17022/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Interpretable and logic-guided RL, one of 15 directions in Deep reinforcement learning methods.
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 19 Oct 2025.

## Placed in

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

## Builds on (most cited first, 9 of 9)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [Exploration by Random Network Distillation](https://mlsanity.com/paper/1810.12894/) (2018)
- [Self-Supervised Exploration via Disagreement](https://mlsanity.com/paper/1906.04161/) (2019)
- [Never Give Up: Learning Directed Exploration Strategies](https://mlsanity.com/paper/2002.06038/) (2020)
- [RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments](https://mlsanity.com/paper/2002.12292/) (2020)
- [Contrastive Reinforcement Learning of Symbolic Reasoning Domains](https://mlsanity.com/paper/2106.09146/) (2021)
- [Symbolic Equation Solving via Reinforcement Learning](https://mlsanity.com/paper/2401.13447/) (2024)
- [RLLTE: Long-Term Evolution Project of Reinforcement Learning](https://mlsanity.com/paper/2309.16382/) (2023)

## Abstract (arXiv)

We explore if RL can be useful for symbolic mathematics. Previous work showed contrastive learning can solve linear equations in one variable. We show model-free PPO citeschulman2017proximal augmented with curiosity-based exploration and graph-based actions can solve nonlinear equations such as those involving radicals, exponentials, and trig functions. Our work suggests curiosity-based exploration may be useful for general symbolic reasoning tasks.