Test-Time Scaling of Reasoning Models for Machine Translation
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.
68f78566a7b9b96b2c13eda4
Zihao Li
claimed_verified
2025-10-21T14:00:18.670Z
6514ba89f95f39fd02a949da
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/3NqhopFY4bQIMxUKNN4es.jpeg
Zihao Li
user
Zihao-Li
68f78566a7b9b96b2c13eda5
Shaoxiong Ji
68f78566a7b9b96b2c13eda6
Jörg Tiedemann
Test-Time Scaling of Reasoning Models for Machine Translation
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.
Test-time scaling improves translation quality in domain-specific models and post-editing but offers limited benefits for general-purpose models in direct translation.
Reasoning Models machine translation test-time scaling direct translation forced-reasoning extrapolation post-editing domain-specific fine-tuning reasoning depth self-correction