TL;DR#
Recent advancements in large language models (LLMs) show promise in various reasoning tasks. However, incorporating human-like reasoning into machine translation (MT) remains underexplored. Existing methods either use fixed reasoning chains tailored to specific MT tasks or rely on synthesized chains unaligned with human strategies, leading to limited adaptability and potential forgetting of general abilities.
To address these issues, this paper introduces R1-Translator (R1-T1), a novel framework that fully incentivizes reasoning-based translation using reinforcement learning (RL). The approach extends reasoning-based translation beyond specific tasks to general MT, supporting diverse tasks and languages. It formalizes expert-curated reasoning templates and enables self-evolving reasoning discovery through RL. Experiments demonstrate improved translation performance, especially in unseen languages, while preserving multilingual abilities.
Key Takeaways#
Why does it matter?#
This paper is important for researchers because it bridges the gap between advanced LLMs and practical MT challenges. By introducing a human-aligned, RL-driven framework, it provides a robust and adaptable solution for improving translation quality and multilingual capabilities. It also opens new avenues for research in self-evolving CoTs and reasoning-driven MT systems.
Visual Insights#
Full paper#
