Paper Detail
Hanxu Hu, Zdeněk Šnajdr, Pinzhen Chen, Jannis Vamvas, Rico Sennrich
Prior work has shown that large language models (LLMs) can translate unseen or low-resource languages by undergoing continued training or even by encoding a grammar book in their context. However, both methods typically overfit specific languages, with limited zero-shot transfer at test time. To translate extremely low-resource languages at scale, we argue that LLMs must acquire the meta-skill of utilizing in-context linguistic knowledge rather than memorizing specific languages. In this paper, we propose a reinforcement learning (RL) approach to unseen language translation given rich linguistic context, using a surface-level translation metric (chrF) as the reward. Empirically, despite the lightweight reward, our RL-trained models effectively extract and apply relevant linguistic information from the provided context, leading to better translations on completely unseen languages than in-context learning or supervised fine-tuning. Our analyses suggest that outcome-based RL can extend beyond conventional reasoning tasks like math and coding to serve as a recipe for language learning from context.
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@misc{hu2026reinforcement,
title = {Reinforcement Learning Elicits Contextual Learning of Unseen Language Translation},
author = {Hanxu Hu and Zdeněk Šnajdr and Pinzhen Chen and Jannis Vamvas and Rico Sennrich},
year = {2026},
abstract = {Prior work has shown that large language models (LLMs) can translate unseen or low-resource languages by undergoing continued training or even by encoding a grammar book in their context. However, both methods typically overfit specific languages, with limited zero-shot transfer at test time. To translate extremely low-resource languages at scale, we argue that LLMs must acquire the meta-skill of utilizing in-context linguistic knowledge rather than memorizing specific languages. In this paper, },
url = {https://huggingface.co/papers/2606.06428},
keywords = {large language models, reinforcement learning, in-context learning, supervised fine-tuning, chrF, linguistic context, zero-shot transfer, meta-skill, code available, huggingface daily},
eprint = {2606.06428},
archiveprefix = {arXiv},
}
{}