Unilaw-R1: A Large Language Model for Legal Reasoning with Reinforcement Learning and Iterative Inference

2025-10-11

AI Summary

To address three key challenges in complex legal reasoning—insufficient domain-specific knowledge, unreliable logical consistency, and poor generalization across legal tasks—this paper introduces UniLaw-R1, the first open-source 7B-parameter large language model explicitly optimized for legal reasoning. Methodologically, we propose a two-stage training paradigm: (i) supervised fine-tuning (SFT) on 17K high-quality legal chain-of-thought samples, followed by (ii) reinforcement learning (RL)-based joint optimization, augmented with an iterative reasoning mechanism. We further construct Unilaw-R1-Eval, a dedicated benchmark for rigorous evaluation. Experimental results demonstrate that UniLaw-R1 achieves an average 6.6% improvement over Qwen-2.5-7B-Instruct on LawBench and LexEval, matching the performance of the 32B-parameter DeepSeek-R1-Distill-Qwen-32B (54.9%). The model significantly enhances both accuracy and interpretability in legal reasoning tasks.

Technology Category

• Natural Language Processing: (Large) Language Models
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Reasoning-focused large language models (LLMs) are rapidly evolving across various domains, yet their capabilities in handling complex legal problems remains underexplored. In this paper, we introduce Unilaw-R1, a large language model tailored for legal reasoning. With a lightweight 7-billion parameter scale, Unilaw-R1 significantly reduces deployment cost while effectively tackling three core challenges in the legal domain: insufficient legal knowledge, unreliable reasoning logic, and weak business generalization. To address these issues, we first construct Unilaw-R1-Data, a high-quality dataset containing 17K distilled and screened chain-of-thought (CoT) samples. Based on this, we adopt a two-stage training strategy combining Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), which significantly boosts the performance on complex legal reasoning tasks and supports interpretable decision-making in legal AI applications. To assess legal reasoning ability, we also introduce Unilaw-R1-Eval, a dedicated benchmark designed to evaluate models across single- and multi-choice legal tasks. Unilaw-R1 demonstrates strong results on authoritative benchmarks, outperforming all models of similar scale and achieving performance on par with the much larger DeepSeek-R1-Distill-Qwen-32B (54.9%). Following domain-specific training, it also showed significant gains on LawBench and LexEval, exceeding Qwen-2.5-7B-Instruct (46.6%) by an average margin of 6.6%.

Problem

• Addresses insufficient legal knowledge in large language models
• Solves unreliable reasoning logic for complex legal problems
• Improves weak business generalization in legal AI applications

Innovation

• Uses reinforcement learning for legal reasoning
• Employs iterative inference to improve logic
• Combines supervised fine-tuning with reinforcement learning