ReGal: A First Look at PPO-based Legal AI for Judgment Prediction and Summarization in India

2025-12-19

AI Summary

This work addresses two high-stakes legal AI tasks in the Indian judicial context—court judgment prediction and explanation (CJPE) and abstractive summarization of lengthy legal documents. Method: We propose ReGal, the first framework to integrate PPO-based reinforcement learning with AI feedback (RLAIF) for Indian legal AI, combining multi-task instruction tuning and domain-specific prompt engineering. It establishes a joint reasoning-and-generation optimization paradigm to tackle reward alignment, legal language modeling, and domain adaptation. Contribution/Results: Although ReGal slightly underperforms supervised baselines on standard automatic metrics, it substantially enhances output interpretability—generating logically coherent judgment reasoning chains and high-quality summaries. Human evaluation in a closed-loop setting confirms its improved trustworthiness and faithfulness. This work provides a novel, empirically grounded pathway toward reliable and interpretable large language models for legal applications.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Applying reinforcement learning to legal AI tasks
• Evaluating PPO-based framework for judgment prediction and summarization
• Addressing challenges like reward alignment and legal language complexity

Innovation

• PPO-based reinforcement learning for legal AI
• Multi-task instruction tuning with RLAIF framework
• Adaptive legal reasoning for judgment prediction and summarization