GPT-OSS-20B is an open-weight reasoning model that thinks in English. Ask it to reason in French, German, Spanish, or Italian and it acknowledges the instruction, then produces its chain-of-thought in English anyway. On a 50-example multilingual reasoning test, the base model reasoned in the correct target language only 22 percent of the time. That 22 percent was almost entirely the English examples (English 100 percent, French 0 percent, German 10 percent, Spanish 0 percent, Italian 0 percent).

For teams deploying reasoning models for non-English users, that gap matters. A user who asks a question in French expects the model to reason and answer in French, not to switch to English mid-thought. Closing that gap is a fine-tuning problem. It raises a question every team faces once they start customizing open-weight models: supervised fine-tuning (SFT), reinforcement learning with Group Relative Policy Optimization (GRPO), or both?

In this post, we walk through how we fine-tuned GPT-OSS-20B for multilingual reasoning on Amazon SageMaker HyperPod. We show when SFT alone was sufficient and when GRPO added measurable value on top. To understand more broadly when GRPO helps, we also ran a controlled Qwen2.5-7B experiment across two tasks with different capability gaps. The short answer: SFT does most of the work, GRPO refines when there is headroom to refine, and matching the technique to the capability gap is what decides the outcome.

This section covers the multilingual reasoning task and dataset, how SFT and GRPO complement each other, and the training stack we ran on Amazon SageMaker HyperPod.

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