Reference-Free Post-Training of Open Large Language Models for Multilingual Machine Translation

We study reference-free post-training for multilingual machine translation with open large language models. Starting from the supervised-finetuned MiLMMT-46-v0.1 models, we apply Group Relative Policy Optimization (GRPO) with a reward that averages two reference-free quality estimation models and is gated by language identification. We then linearly interpolate the supervised fine-tuning (SFT) and reinforcement learning (RL) model checkpoints to obtain MiLMMT-46-v1.0. Across 46 languages, the re

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ethics.ai (10 August 2026), “Reference-Free Post-Training of Open Large Language Models for Multilingual Machine Translation,” evidence record 18376, https://ethics.ai/record/18376 (originally published by HuggingFace Daily Papers).

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