@inproceedings{hu-etal-2026-towards-better,
title = "Towards Better Legal Reasoning {LLM}s: Signal Balancing and Reward Scheduling",
author = "Hu, Yinghao and
Xiao, Qian and
Huang, Yuting and
Song, Kaisong and
Wu, Yiquan and
Li, Ang and
Cao, Junjie and
Lin, Jun and
Kuang, Kun and
Wu, Fei",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1273/",
pages = "26924--26944",
ISBN = "979-8-89176-462-0",
abstract = "In the legal domain, LLMs can improve reliability and interpretability by generating explicit reasoning traces. However, training LLMs to produce high-quality reasoning traces remains challenging. Existing supervised fine-tuning methods struggle when reasoning traces are significantly longer than final answers, as the learning signal for the answer becomes diluted. Meanwhile, reinforcement learning methods such as Group Relative Policy Optimization also have several limitations, including costly reward design, performance plateaus, and reward hacking. We therefore propose a two-stage training framework. In Stage I, JurisCoT-SFT employs a length-normalized dual objective to balance learning signals between reasoning traces and final answers. In Stage II, Lifecycle-Aware Backtrackable Policy Optimization dynamically evaluates and retires auxiliary reward signals based on their impact on final evaluation objectives, enabling efficient reward utilization. Our framework substantially improves legal reasoning performance on relevant tasks selected from multiple legal benchmarks, including ChJGBench, LawBench, and LegalBench. Experimental results demonstrate consistent improvements in both reasoning trace quality and final answer accuracy, while our models remain competitive with larger models."
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<abstract>In the legal domain, LLMs can improve reliability and interpretability by generating explicit reasoning traces. However, training LLMs to produce high-quality reasoning traces remains challenging. Existing supervised fine-tuning methods struggle when reasoning traces are significantly longer than final answers, as the learning signal for the answer becomes diluted. Meanwhile, reinforcement learning methods such as Group Relative Policy Optimization also have several limitations, including costly reward design, performance plateaus, and reward hacking. We therefore propose a two-stage training framework. In Stage I, JurisCoT-SFT employs a length-normalized dual objective to balance learning signals between reasoning traces and final answers. In Stage II, Lifecycle-Aware Backtrackable Policy Optimization dynamically evaluates and retires auxiliary reward signals based on their impact on final evaluation objectives, enabling efficient reward utilization. Our framework substantially improves legal reasoning performance on relevant tasks selected from multiple legal benchmarks, including ChJGBench, LawBench, and LegalBench. Experimental results demonstrate consistent improvements in both reasoning trace quality and final answer accuracy, while our models remain competitive with larger models.</abstract>
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%0 Conference Proceedings
%T Towards Better Legal Reasoning LLMs: Signal Balancing and Reward Scheduling
%A Hu, Yinghao
%A Xiao, Qian
%A Huang, Yuting
%A Song, Kaisong
%A Wu, Yiquan
%A Li, Ang
%A Cao, Junjie
%A Lin, Jun
%A Kuang, Kun
%A Wu, Fei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F hu-etal-2026-towards-better
%X In the legal domain, LLMs can improve reliability and interpretability by generating explicit reasoning traces. However, training LLMs to produce high-quality reasoning traces remains challenging. Existing supervised fine-tuning methods struggle when reasoning traces are significantly longer than final answers, as the learning signal for the answer becomes diluted. Meanwhile, reinforcement learning methods such as Group Relative Policy Optimization also have several limitations, including costly reward design, performance plateaus, and reward hacking. We therefore propose a two-stage training framework. In Stage I, JurisCoT-SFT employs a length-normalized dual objective to balance learning signals between reasoning traces and final answers. In Stage II, Lifecycle-Aware Backtrackable Policy Optimization dynamically evaluates and retires auxiliary reward signals based on their impact on final evaluation objectives, enabling efficient reward utilization. Our framework substantially improves legal reasoning performance on relevant tasks selected from multiple legal benchmarks, including ChJGBench, LawBench, and LegalBench. Experimental results demonstrate consistent improvements in both reasoning trace quality and final answer accuracy, while our models remain competitive with larger models.
%U https://aclanthology.org/2026.findings-emnlp.1273/
%P 26924-26944
Markdown (Informal)
[Towards Better Legal Reasoning LLMs: Signal Balancing and Reward Scheduling](https://aclanthology.org/2026.findings-emnlp.1273/) (Hu et al., Findings 2026)
ACL
- Yinghao Hu, Qian Xiao, Yuting Huang, Kaisong Song, Yiquan Wu, Ang Li, Junjie Cao, Jun Lin, Kun Kuang, and Fei Wu. 2026. Towards Better Legal Reasoning LLMs: Signal Balancing and Reward Scheduling. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 26924–26944, Budapest, Hungary. Association for Computational Linguistics.