This paper presents an early exploration of reinforcement learning methodologies for legal AI in the Indian context. We introduce Reinforcement Learning-based Legal Reasoning (ReGal), a framework that integrates Multi-Task Instruction Tuning with Reinforcement Learning from AI Feedback (RLAIF) using Proximal Policy Optimization (PPO). Our approach is evaluated across two critical legal tasks: (i) Court Judgment Prediction and Explanation (CJPE), and (ii) Legal Document Summarization. Although the framework underperforms on standard evaluation metrics compared to supervised and proprietary models, it provides valuable insights into the challenges of applying RL to legal texts. These challenges include reward model alignment, legal language complexity, and domain-specific adaptation. Through empirical and qualitative analysis, we demonstrate how RL can be repurposed for high-stakes, long-document tasks in law. Our findings establish a foundation for future work on optimizing legal reasoning pipelines using reinforcement learning, with broader implications for building interpretable and adaptive legal AI systems.

翻译：本文对印度背景下的法律人工智能强化学习方法进行了早期探索。我们提出了基于强化学习的法律推理框架（ReGal），该框架通过近端策略优化（PPO）将多任务指令微调与AI反馈强化学习（RLAIF）相结合。我们在两项关键法律任务上评估了该框架：（1）法庭判决预测与解释（CJPE），（2）法律文档摘要生成。尽管相较于监督学习和专有模型，该框架在标准评估指标上表现欠佳，但它为强化学习在法律文本应用中的挑战提供了重要洞见，包括奖励模型对齐、法律语言复杂性及领域适应性等问题。通过实证与定性分析，我们展示了如何将强化学习重新应用于法律领域的高风险长文档任务。本研究为未来利用强化学习优化法律推理流程奠定了基础，并对构建可解释、自适应的法律AI系统具有更广泛的启示意义。