# Seed-X: Building Strong Multilingual Translation LLM with 7B Parameters

arXiv: https://arxiv.org/abs/2507.13618 · HTML: https://mlsanity.com/paper/2507.13618/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 56 references that are in the map.
- First posted on arXiv 18 Jul 2025.

## Builds on (most cited first, 30 of 40)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [WizardCoder: Empowering Code Large Language Models with Evol-Instruct](https://mlsanity.com/paper/2306.08568/) (2023)
- [DeepSeek LLM: Scaling Open-Source Language Models with Longtermism](https://mlsanity.com/paper/2401.02954/) (2024)
- [The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation](https://mlsanity.com/paper/2106.03193/) (2021)
- [MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning](https://mlsanity.com/paper/2309.05653/) (2023)
- [Scaling Relationship on Learning Mathematical Reasoning with Large Language Models](https://mlsanity.com/paper/2308.01825/) (2023)
- [Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation](https://mlsanity.com/paper/2401.08417/) (2024)
- [XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning](https://mlsanity.com/paper/2005.00333/) (2020)
- [Few-shot Learning with Multilingual Language Models](https://mlsanity.com/paper/2112.10668/) (2021)
- [xCOMET: Transparent Machine Translation Evaluation through Fine-grained Error Detection](https://mlsanity.com/paper/2310.10482/) (2023)
- [PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification](https://mlsanity.com/paper/1908.11828/) (2019)
- [Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine](https://mlsanity.com/paper/2301.08745/) (2023)
- [Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis](https://mlsanity.com/paper/2304.04675/) (2023)
- [MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning](https://mlsanity.com/paper/2310.03731/) (2023)
- [How do Large Language Models Handle Multilingualism?](https://mlsanity.com/paper/2402.18815/) (2024)
- [A Survey of Large Language Models in Medicine: Progress, Application, and Challenge](https://mlsanity.com/paper/2311.05112/) (2023)

## Built on by (most cited first, 21 of 21)

- [GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models](https://mlsanity.com/paper/2508.06471/) (2025)
- [Hunyuan-MT Technical Report](https://mlsanity.com/paper/2509.05209/) (2025)
- [Test-Time Scaling of Reasoning Models for Machine Translation](https://mlsanity.com/paper/2510.06471/) (2025)
- [Mending the Holes: Mitigating Reward Hacking in Reinforcement Learning for Multilingual Translation](https://mlsanity.com/paper/2603.13045/) (2026)
- [Understanding Post-Training Structural Changes in Large Language Models](https://mlsanity.com/paper/2509.17866/) (2025)
- [GRRM: Group Relative Reward Modeling for Machine Translation](https://mlsanity.com/paper/2602.14028/) (2026)
- [Beyond Literal Translation: Evaluating Cultural Effectiveness in Social Media UGC](https://mlsanity.com/paper/2605.25626/) (2026)
- [HY-MT1.5 Technical Report](https://mlsanity.com/paper/2512.24092/) (2025)
- [Shifting the Breaking Point of Flow Matching for Multi-Instance Editing](https://mlsanity.com/paper/2602.08749/) (2026)
- [Optimizing Korean-Centric LLMs via Token Pruning](https://mlsanity.com/paper/2604.16235/) (2026)
- [Cultivar: A Contrastive and Locale-Oriented Translation Benchmark for Investigating Contamination and Localisation Robustness](https://mlsanity.com/paper/2608.09766/) (2026)
- [Last Translation Benchmark](https://mlsanity.com/paper/2609.04173/) (2026)
- [SmolKalam: Ensemble Quality-Filtered Translation at Scale for High Quality Arabic Post-Training Data](https://mlsanity.com/paper/2511.18411/) (2025)
- [OmniFusion: Simultaneous Multilingual Multimodal Translations via Modular Fusion](https://mlsanity.com/paper/2512.00234/) (2025)
- [Fluent Alignment with Disfluent Judges: Post-training for Lower-resource Languages](https://mlsanity.com/paper/2512.08777/) (2025)
- [DEEP: Docker-based Execution and Evaluation Platform](https://mlsanity.com/paper/2602.19583/) (2026)
- [From Words to Worlds: Benchmarking Cross-Cultural Cultural Understanding in Machine Translation](https://mlsanity.com/paper/2603.17303/) (2026)
- [VEPO: Variable Entropy Policy Optimization for Low-Resource Language Foundation Models](https://mlsanity.com/paper/2603.19152/) (2026)
- [Two-Stage Multi-Modal Fusion with Adaptive Alignment for Action Quality Assessment](https://mlsanity.com/paper/2607.07438/) (2026)
- [Studying quantization trade-offs for efficient inference deployment in machine translation](https://mlsanity.com/paper/2607.29397/) (2026)
- [Drift-Constrained Optimization: Only Direction Matters in Fine-Tuning Instruct Models](https://mlsanity.com/paper/2609.13680/) (2026)

## Abstract (arXiv)

Multilingual translation stands as a challenging task for large language models (LLMs) to handle intricate language patterns and stilted translations that arise in automated translations. In this paper, we introduce Seed-X, a family of open-source LLMs comprising instruct and reasoning models, pushing the limits of translation capability with 7B parameter size. The base model is pre-trained on a diverse, high-quality dataset encompassing both monolingual and bilingual content across 28 languages, harnessing the full potential of multilingual data. The instruct model is then finetuned to translate by Chain-of-Thought (CoT) reasoning and further enhanced through reinforcement learning (RL) to achieve better generalization across diverse language pairs. Seed-X achieves performance comparable to leading closed-source models, including Gemini-2.5 and GPT-4o, across 28 languages, and significantly outperforms larger open-source models in both automatic metrics and human evaluations. We share the best practices through our optimization process, and make the parameter public available for advancing translation research and applications.