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AWS fine-tunes GPT-OSS-20B for multilingual reasoning on SageMaker HyperPod

By PulseAugur Editorial · [1 sources] ·

Amazon Web Services (AWS) has detailed a process for fine-tuning the GPT-OSS-20B open-weight reasoning model to improve its performance in multiple languages. The base model struggled with multilingual reasoning, often reverting to English. By employing Supervised Fine-Tuning (SFT) and Group Relative Policy Optimization (GRPO) on Amazon SageMaker HyperPod, AWS demonstrated significant improvements in the model's ability to reason and answer in French, German, Spanish, and Italian. The study also included a control experiment with Qwen2.5-7B to further understand the effectiveness of GRPO. AI

IMPACT Demonstrates methods to improve multilingual capabilities of open-weight models, potentially broadening their applicability.

RANK_REASON Blog post detailing a specific fine-tuning methodology for an open-weight model. [lever_c_demoted from research: ic=1 ai=1.0]

Read on AWS Machine Learning Blog →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AWS fine-tunes GPT-OSS-20B for multilingual reasoning on SageMaker HyperPod

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Blog post detailing a specific fine-tuning methodology for an open-weight model. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. AWS Machine Learning Blog TIER_1 English(EN) · Mijanur Palash ·

    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 h…

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