JPO: Juris Policy Optimization for Structured Legal Reasoning in Criminal Judgment Prediction

2026-08-30

AI Summary

本文针对刑事判决预测中的结构化法律推理问题，提出了一种基于强化学习的方法Juris Policy Optimization (JPO)，通过优化法律预测质量、推理结构完整性和跨步骤一致性来改进模型性能。

Abstract

Criminal judgment prediction requires models to infer statutory articles, charges, and sentencing outcomes from case facts. Unlike standard classification tasks, it involves a structured reasoning process in which statutes should be matched with facts, charges should be justified by statutes, and sentencing outcomes should remain consistent with charges. Existing approaches optimize final labels, and while some have attempted to evaluate reasoning quality, their evaluations are indirect, often relying on LLM-generated rubrics that reflect model-internal preferences rather than the inherent logical structure of legal adjudication. We propose Juris Policy Optimization (JPO), a post-training framework for structured legal reasoning in Chinese criminal judgment prediction. JPO first uses teacher-generated rationales to supervise a standardized four-step reasoning process, and then applies reinforcement learning with a composite reward over legal prediction quality, reasoning structure completeness, and cross-step consistency. JPO further introduces token-level advantage reweighting and adaptive clipping for legally salient reasoning segments. Experiments on multiple open-source language models and three Chinese legal benchmarks show that JPO consistently improves both judgment prediction and reasoning quality over supervised fine-tuning and reinforcement learning baselines.

Problem

• criminal judgment prediction
• structured reasoning
• legal adjudication
• reasoning quality
• reinforcement learning

Innovation

• Juris Policy Optimization
• structured legal reasoning
• reinforcement learning
• composite reward
• adaptive clipping