Reinforcement Learning Elicits Contextual Learning of Unseen Language Translation
For machine translation of extremely low-resource languages, this work demonstrates that RL can elicit the meta-skill of using in-context linguistic knowledge, outperforming existing methods.
The paper proposes a reinforcement learning approach for unseen language translation using chrF as reward, achieving better translations on completely unseen languages than in-context learning or supervised fine-tuning.
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.