AWS published a technical walkthrough on fine-tuning the GPT-OSS-20B model for multilingual reasoning using Amazon SageMaker HyperPod. The post explains how to decide between supervised fine-tuning (SFT) and reinforcement learning with GRPO. According to the description, SFT increased reasoning-language accuracy from 22% to 100%, while GRPO provided additional measurable improvements in cases where accuracy headroom still existed.
Why it matters
The reported accuracy change illustrates how fine-tuning methods can affect a model’s performance on multilingual reasoning tasks, and the comparison offers a basis for selecting between SFT and GRPO depending on the available improvement margin.
Who should care
Machine learning practitioners and teams working with open-weight models on AWS infrastructure who are evaluating fine-tuning approaches for reasoning tasks.