Cold Starts and Hard Cases: A Two-Stage SFT-RLVR Approach for Legal Machine Translation (Just-NLP L-MT shared task)

AbstractThis paper details our system for the JUST-NLP 2025 Shared Task on English-to-Hindi Legal Machine Translation. We propose a novel two-stage, data-centric approach. First, we annotate the training data by translation difficulty and create easy and hard subsets.We perform SFT on the easier subset to establish a robust “cold start”. Then, we apply RLVR exclusively on the harder subset, using machine translation metrics as reward signals. This strategy allowed our system to significantly outperform strong baselines, demonstrating the capability of our systems for machine translation tasks. Source code and model weights are available at https://github.com/ppaolong/FourCorners-JustNLP-MT-Shared-Task