Analysis Result

%https://arxiv.org/pdf/2505.03977? \chapter{Materials and Methods} This chapter details the methodology for the Seriguela pipeline and is intended to describe: the data engineering process for generating, cleaning, and augmenting the mathematical expression dataset; the model's training methodology, including the initial supervised fine-tuning and the subsequent reinforcement learning refinement with Proximal Policy Optimization (PPO); the benchmark datasets and evaluation metrics used to assess accuracy and model complexity; and the essential implementation details regarding the software and hardware environment. \subsection{Conceptual Pipeline} \begin{figure}[htbp] \centering \includegraphics[width=0.9\linewidth]{figures/Overview.pdf} % Adjusted width for better margins \caption[End-to-end pipeline of the Seriguela Pipeline]{End-to-end pipeline of the Seriguela Pipeline.} \label{fig:pipeline} \end{figure} \section{Overview of the Proposed Seriguela Pipeline} As illustrated in Figure~\ref{fig:pipeline}, our approach implements a three-stage methodology for symbolic regression: \begin{enumerate} \item \textbf{Data Engineering Phase}: A dataset of expressions is generated, evaluated to remove invalid expressions, and finally augmented with prompt format and new variables.