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.
What happened
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.
Why it matters
The development may change operating conditions or market expectations around Amazon. Further confirmation and measurable outcomes matter.
Affected entities
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1 reports · 1 original report · 1 independent
- AWS Machine Learning BlogPrimary source · Supports · EN · 100%Fine-tuning GPT-OSS-20B for multilingual reasoning on Amazon SageMaker HyperPod ↗
Claims
- Fine-tuning GPT-OSS-20B for multilingual reasoning on Amazon SageMaker HyperPod Observed
Conflicts
No material conflict detected in the available evidence.
Timeline
- First reported
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