使用GRPO对护照数据提取进行推理模型的微调

Extracting structured data from passports isn’t just about OCR—it’s about reasoning. Traditional OCR methods struggle with formatting inconsistencies, multilingual text, and real-world variations, but fine-tuning a model with GRPO enhances contextual understanding, improving accuracy and adaptability. This blog helps developers fine-tune a reasoning model with GRPO to optimize passport data extraction, covering key challenges, implementation techniques, and best practices.

从护照中提取结构化数据不仅仅是OCR——而是推理。传统的OCR方法在格式不一致、多语言文本和现实世界变体方面存在困难，但使用GRPO微调模型可以增强上下文理解，提高准确性和适应性。此博客帮助开发人员使用GRPO微调推理模型，以优化护照数据提取，涵盖关键挑战、实施技术和最佳实践。

Fine-tuning a language model isn’t just about feeding it data and hoping for the best. If you’re extracting structured data—like passport details—you need a model that reasons through the problem, not one that just memorizes patterns. That’s where Group Relative Policy Optimization (GRPO) comes in.

微调语言模型不仅仅是提供数据并寄希望于最佳结果。如果您要提取结构化数据——例如护照详细信息——您需要一个能够推理问题的模型，而不是一个仅仅记忆模式的模型。这就是Group Relative Policy Optimization (GRPO)的用武之地。

In this post, we’ll walk through fine-tuning a reasoning model for passport data extraction using GRPO. We’ll start with Supervised Fine-Tuning (SFT) and then refine it using reinforcement learning (RL) to improve accuracy and reasoning.

在这篇文章中，我们将通过GRPO对护照数据提取的推理模型进行微调。我们将从监督微调（SFT）开始，然后使用强化学习（RL）进行优化，以提高准确性和推理能力。

We’ll use:

我们将使用：

• Base Model: Qwen/Qwen2.5-1.5B-Instruct

  基础模型： Qwen/Qwen2.5-1.5B-Instruct

• Dataset: Custom Passport EN dataset

  数据集： 自定义护照英文数据集

• Training Method: SFT + GRPO

  训练方法: SFT + GRPO

All code at Github

所有代码在Github

Supervised fine-tuning ( or SFT) is effective for training a baseline model, but it struggles with generalization. When extracting structured data, slight variations in input format can lead to errors. Standard SFT lacks the adaptive reasoning needed to handle these cases effectively.

监督微调（或SFT）对于训练基线模型是有效的，但在泛化方面存在困难。在提取结构化数据时，输入格式的轻微变化可能导致错误。标准SFT缺乏处理这些情况所需的自适应推理能力。

This is where GRPO improves the model. The DeepSeekMath paper introduces GRPO as an RL post-tr...