ReGal: A First Look at PPO-based Legal AI for Judgment Prediction and Summarization in India
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arXiv
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| Format: | Preprint |
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2025
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| author | Nigam, Shubham Kumar Tyagi, Tanuj Shukla, Siddharth Guru, Aditya Kumar Patnaik, Balaramamahanthi Deepak Khanna, Danush Shallum, Noel Ghosh, Kripabandhu Bhattacharya, Arnab |
| author_facet | Nigam, Shubham Kumar Tyagi, Tanuj Shukla, Siddharth Guru, Aditya Kumar Patnaik, Balaramamahanthi Deepak Khanna, Danush Shallum, Noel Ghosh, Kripabandhu Bhattacharya, Arnab |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_18014 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ReGal: A First Look at PPO-based Legal AI for Judgment Prediction and Summarization in India Nigam, Shubham Kumar Tyagi, Tanuj Shukla, Siddharth Guru, Aditya Kumar Patnaik, Balaramamahanthi Deepak Khanna, Danush Shallum, Noel Ghosh, Kripabandhu Bhattacharya, Arnab Computation and Language Artificial Intelligence Machine Learning 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. |
| title | ReGal: A First Look at PPO-based Legal AI for Judgment Prediction and Summarization in India |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2512.18014 |