Reinforcement Learning Elicits Contextual Learning of Unseen Language Translation

• URL: http://arxiv.org/abs/2606.06428v1
• Date: Thu, 04 Jun 2026 17:32:06 GMT
• Title: Reinforcement Learning Elicits Contextual Learning of Unseen Language Translation
• Abstract summary: We propose a reinforcement learning (RL) approach to unseen language translation given rich linguistic context.<n>Despite the lightweight reward, our RL-trained models effectively extract and apply relevant linguistic information from the provided context.<n>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.
• Score: 35.3382718505629
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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, 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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