The Price of Reasoning: Cost-Quality Tradeoffs in Reinforcement Learning for Neural Machine Translation

TL;DR - This paper studies whether explicit reasoning traces improve RLVR-trained neural machine translation and at what computational cost. Reasoning during inference improves translation quality but increases output-token usage.

• Systematically omits reasoning traces during either training or inference to isolate their effects.
• Finds that reasoning is particularly beneficial during inference.
• Evaluates the tradeoff between higher translation quality and increased token-generation costs.
• Examines RLVR for specialized translation settings, including legal documents.