# Investigating Test-Time Scaling with Reranking for Machine Translation

arXiv: https://arxiv.org/abs/2509.19020 · HTML: https://mlsanity.com/paper/2509.19020/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning.
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 23 Sep 2025.

## Placed in

- LLM reasoning / [Test-time compute and efficient thinking](https://mlsanity.com/lr/test-time-compute-and-efficient-thinking/)

## Builds on (most cited first, 13 of 13)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://mlsanity.com/paper/2408.03314/) (2024)
- [s1: Simple test-time scaling](https://mlsanity.com/paper/2501.19393/) (2025)
- [Qwen2.5-Coder Technical Report](https://mlsanity.com/paper/2409.12186/) (2024)
- [Deep Learning Scaling is Predictable, Empirically](https://mlsanity.com/paper/1712.00409/) (2017)
- [Tower: An Open Multilingual Large Language Model for Translation-Related Tasks](https://mlsanity.com/paper/2402.17733/) (2024)
- [Quality-Aware Decoding for Neural Machine Translation](https://mlsanity.com/paper/2205.00978/) (2022)
- [MT-R1-Zero: Advancing LLM-based Machine Translation via R1-Zero-like Reinforcement Learning](https://mlsanity.com/paper/2504.10160/) (2025)
- [Remedy: Learning Machine Translation Evaluation from Human Preferences with Reward Modeling](https://mlsanity.com/paper/2504.13630/) (2025)
- [Please Translate Again: Two Simple Experiments on Whether Human-Like Reasoning Helps Translation](https://mlsanity.com/paper/2506.04521/) (2025)
- [Adding Chocolate to Mint: Mitigating Metric Interference in Machine Translation](https://mlsanity.com/paper/2503.08327/) (2025)

## Built on by (most cited first, 3 of 3)

- [Test-Time Scaling of Reasoning Models for Machine Translation](https://mlsanity.com/paper/2510.06471/) (2025)
- [What Does LLM Refinement Actually Improve? A Systematic Study on Document-Level Literary Translation](https://mlsanity.com/paper/2605.13368/) (2026)
- [Remedy-R: Generative Reasoning for Machine Translation Evaluation without Error Annotations](https://mlsanity.com/paper/2512.18906/) (2025)

## Abstract (arXiv)

Scaling model parameters has become the de facto strategy for improving NLP systems, but it comes with substantial computational costs. Test-Time Scaling (TTS) offers an alternative by allocating more computation at inference: generating multiple candidates and selecting the best. While effective in tasks such as mathematical reasoning, TTS has not been systematically explored for machine translation (MT). In this paper, we present the first systematic study of TTS for MT, investigating a simple but practical best-of-N framework on WMT24 benchmarks. Our experiments cover six high-resource and one low-resource language pairs, five model sizes (3B-72B), and various TTS compute budget (N up to 1024). Our results show that a) For high-resource languages, TTS generally improves translation quality according to multiple neural MT evaluation metrics, and our human evaluation confirms these gains; b) Augmenting smaller models with large N can match or surpass larger models at N=1 with more compute cost; c) Under fixed compute budgets, larger models are typically more efficient, and TTS can degrade quality due to metric blind spots in low-resource cases.