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

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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.

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

arXiv cs.CL Michael Jungo, Aixiu An 2026-07-21 arXiv:2607.19226 doi:10.1145/3820755.3833407
Public signals OpenAlex citations 0
Providers: Hugging Face · N/A OpenAlex · Citations 0 Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-08-21 14:38:57.704644 UTC

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.
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