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.