EffiReasonTrans: RL-Optimized Reasoning for Code Translation

Code translation is a crucial task in software development and maintenance. While recent advancements in Large Language Models (LLMs) have improved automated code translation accuracy, these gains often come at the cost of increased inference latency–hindering real-world development workflows that involve human-in-the-loop inspection. To address this tradeoff, we propose EffiReasonTrans, a training framework designed to improve translation accuracy while balancing inference latency. We first construct a high-quality reasoning-augmented dataset by prompting a stronger language model DeepSeek-R1 to generate intermediate reasoning and target translations. Each (source code, reasoning, target code) triplet undergoes automated syntax and functionality checks to ensure reliability. Based on this dataset, we employ a two-stage training strategy: supervised fine-tuning on reasoning-augmented samples, followed by reinforcement learning to further enhance accuracy, which also helps balance inference latency. We evaluate EffiReasonTrans on six translation pairs. Experimental results show that EffiReason-Trans consistently improves translation accuracy (up to +49.2% CA and +27.8% CodeBLEU compared to the base model), while reducing the number of generated tokens (up to -19.3%) and lowering inference latency in most cases (up to -29.0%). Ablation studies further confirm the complementary benefits of the two-stage training framework. Additionally, EffiReasonTrans shows improvements of translation accuracy when integrated into agent-based frameworks. Our code and data are available athttps://github.com/DeepSoftwareAnalytics/EffiReasonTrans.