GPT-OSS-20B fine-tuned for multilingual reasoning on SageMaker HyperPod
Fine-tuning GPT-OSS-20B for multilingual reasoning on Amazon SageMaker HyperPod
The real lesson is the decision framework: pick SFT when accuracy targets are clear, and add GRPO only when benchmarks show remaining headroom, avoiding wasted RL compute. Practitioners building multilingual models on their own data can replicate the workflow on SageMaker HyperPod with open weights, no dependency on a closed API.
AWS published a tutorial on fine-tuning OpenAI's open-weight GPT-OSS-20B for multilingual reasoning using Amazon SageMaker HyperPod. The guide compares supervised fine-tuning (SFT) with reinforcement learning using GRPO. SFT lifted reasoning-language accuracy from 22% to 100%, while GRPO delivered measurable additional gains where headroom remained.
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.
