LegalOne：一种用于可靠法律推理的基础模型家族
LegalOne: A Family of Foundation Models for Reliable Legal Reasoning
AI总结：
LegalOne通过三阶段流程提升法律推理能力，结合领域适应、代理推理蒸馏和课程强化学习，在法律任务中取得超越通用LLMs的性能。
摘要：
尽管大型语言模型（LLMs）在一般能力上表现出色，但它们在法律领域中的直接应用往往受到缺乏精确领域知识和进行严谨多步骤司法推理复杂性的限制。为了解决这一差距，我们提出了LegalOne，一种专门针对中国法律领域的基础模型家族。LegalOne通过一个全面的三阶段流程开发，旨在掌握法律推理。首先，在中等训练阶段，我们提出了一种名为Plasticity-Adjusted Sampling（PAS）的方法，以解决领域适应的挑战。这种基于困惑度的调度器在获取新知识和保留原有能力之间取得平衡，有效地建立了稳固的法律基础。其次，在监督微调阶段，我们采用Legal Agentic CoT Distillation（LEAD）来从原始法律文本中提炼出明确的推理过程。与传统的蒸馏方法不同，LEAD利用一种代理工作流程，将复杂的司法过程转化为结构化的推理轨迹，从而确保事实的准确性和逻辑的严谨性。最后，我们实施了一种课程强化学习（RL）策略。通过一个从记忆、理解到推理逐步强化的过程，LegalOne从简单的模式匹配发展为自主且可靠的法律推理。实验结果表明，LegalOne在广泛的法律任务上实现了最先进的性能，通过增强的知识密度和效率，超越了参数数量远大于通用LLMs的模型。我们公开发布了LegalOne的权重和LegalKit评估框架，以推动法律人工智能领域的发展，为在高风险司法应用中部署可信赖和可解释的基础模型铺平道路。
英文摘要：
While Large Language Models (LLMs) have demonstrated impressive general capabilities, their direct application in the legal domain is often hindered by a lack of precise domain knowledge and complexity of performing rigorous multi-step judicial reasoning. To address this gap, we present LegalOne, a family of foundational models specifically tailored for the Chinese legal domain. LegalOne is developed through a comprehensive three-phase pipeline designed to master legal reasoning. First, during mid-training phase, we propose Plasticity-Adjusted Sampling (PAS) to address the challenge of domain adaptation. This perplexity-based scheduler strikes a balance between the acquisition of new knowledge and the retention of original capabilities, effectively establishing a robust legal foundation. Second, during supervised fine-tuning, we employ Legal Agentic CoT Distillation (LEAD) to distill explicit reasoning from raw legal texts. Unlike naive distillation, LEAD utilizes an agentic workflow to convert complex judicial processes into structured reasoning trajectories, thereby enforcing factual grounding and logical rigor. Finally, we implement a Curriculum Reinforcement Learning (RL) strategy. Through a progressive reinforcement process spanning memorization, understanding, and reasoning, LegalOne evolves from simple pattern matching to autonomous and reliable legal reasoning. Experimental results demonstrate that LegalOne achieves state-of-the-art performance across a wide range of legal tasks, surpassing general-purpose LLMs with vastly larger parameter counts through enhanced knowledge density and efficiency. We publicly release the LegalOne weights and the LegalKit evaluation framework to advance the field of Legal AI, paving the way for deploying trustworthy and interpretable foundation models in high-stakes judicial applications.
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