Summary of Generative Pre-trained Transformer For Symbolic Regression Base In-context Reinforcement Learning, by Yanjie Li et al.
Generative Pre-Trained Transformer for Symbolic Regression Base In-Context Reinforcement Learning
by Yanjie Li, Weijun Li, Lina Yu, Min Wu, Jingyi Liu, Wenqiang Li, Meilan Hao, Shu Wei, Yusong Deng
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes FormulaGPT, a novel method that combines the strengths of symbolic regression (SR) algorithms based on genetic programming (GP) or reinforcement learning (RL) with the speed and scalability of Generative Pre-Trained Transformers (GPT). By training GPT using massive sparse reward learning histories from RL-based SR algorithms, the proposed method distills an RL process into a Transformer that can automatically update its policy in context. This approach achieves state-of-the-art performance on over ten datasets, including SRBench, and outperforms four baselines in terms of fitting ability, noise robustness, versatility, and inference efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new method called FormulaGPT to find mathematical formulas from observational data using artificial intelligence. It combines the strengths of two existing methods: genetic programming and reinforcement learning. The goal is to create an algorithm that can quickly find formulas while also being good at handling noisy or unexpected data. The proposed method uses massive amounts of training data to teach a special type of neural network called a Transformer. This allows the algorithm to learn from its mistakes and adapt to new situations more effectively. The results show that FormulaGPT outperforms existing methods in finding mathematical formulas and is more robust to noise. |
Keywords
» Artificial intelligence » Gpt » Inference » Neural network » Regression » Reinforcement learning » Transformer
