Fine-tuning GPT-OSS-20B for multilingual reasoning on Amazon SageMaker HyperPod
Learn how we fine-tuned GPT-OSS-20B for multilingual reasoning on Amazon SageMaker HyperPod, and how to decide between supervised fine-tuning (SFT) and reinforcement learning with GRPO. SFT took reasoning-language accuracy from 22% to 100%, and GRPO added measurable gains where headroom existed.
The article discusses the process of fine-tuning the open-weight reasoning model GPT-OSS-20B for multilingual reasoning on Amazon SageMaker HyperPod. This model is capable of reasoning in English, but when asked to reason in other languages such as French, German, Spanish, or Italian, it defaults to English in its chain-of-thought responses.
This can be problematic for users who expect the model to provide answers in the language they requested. Fine-tuning aims to address this issue, and the article explains how supervised fine-tuning (SFT) and reinforcement learning with Group Relative Policy Optimization (GRPO) complement each other in this context.
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