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Looped Translation: Fine-tuning LoopLM for NMT

First study of whether iterative latent computation helps machine translation, evaluated on WMT24++.

Quick Start

pip install -r requirements.txt
python train_ouro_2.6b.py
python train_qwen_3b.py
python eval_baseline.py
python eval_finetuned.py
python analyze_results.py

Files

  • train_ouro_2.6b.py - QLoRA fine-tune Ouro-2.6B
  • train_qwen_3b.py - QLoRA fine-tune Qwen2.5-3B
  • eval_baseline.py - Phase 1: Baseline evaluation
  • eval_finetuned.py - Phase 2: Fine-tuned evaluation
  • analyze_results.py - Phase 3: Analysis

Training Data

davidmeikle/opus-translation-train-en-de-cs

Citation

@article{zhu2025scaling,
  title={Scaling Latent Reasoning via Looped Language Models},
  author={Zhu, Rui-Jie and others},
  journal={arXiv preprint arXiv:2510.25741},
  year={2025}
}
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Paper for davidmeikle/looped-translation-ouro-wmt24pp