Test-Time Scaling of Reasoning Models for Machine Translation
Authors: name: Zihao Li, name: Shaoxiong Ji, name: Jörg Tiedemann
Organization: Helsinki-NLP Research Group

Test-time scaling (TTS) has enhanced the performance of Reasoning Models
(RMs) on various tasks such as math and coding, yet its efficacy in machine
translation (MT) remains underexplored. This paper investigates whether
increased inference-time computation improves translation quality. We evaluate
12 RMs across a diverse suite of MT benchmarks spanning multiple domains,
examining three scenarios: direct translation, forced-reasoning extrapolation,
and post-editing. Our findings show that for general-purpose RMs, TTS provides
limited and inconsistent benefits for direct translation, with performance
quickly plateauing. However, the effectiveness of TTS is unlocked by
domain-specific fine-tuning, which aligns a model's reasoning process with task
requirements, leading to consistent improvements up to an optimal,
self-determined reasoning depth. We also find that forcing a model to reason
beyond its natural stopping point consistently degrades translation quality. In
contrast, TTS proves highly effective in a post-editing context, reliably
turning self-correction into a beneficial process. These results indicate that
the value of inference-time computation in MT lies not in enhancing single-pass
translation with general models, but in targeted applications like multi-step,
self-correction workflows and in conjunction with task-specialized models.