Fine-tuning GPT-OSS-20B for multilingual reasoning on Amazon SageMaker HyperPod
TL;DR
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.
Nauti's Take
Small teams should first test whether the reported 100% transfers to their own languages, datasets, and evaluation prompts. GRPO should come later, once a measurable gap remains and the added training effort improves a concrete production workflow.
Since AWS describes a single experiment, these results are not yet a general benchmark.
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