{"author_name":"revcomm-tech","author_url":"https://blog.hatena.ne.jp/revcomm-tech/","image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/r/revcomm-tech/20260603/20260603114637.png","provider_name":"Hatena Blog","width":"100%","url":"https://tech.revcomm.co.jp/introduction-to-grpo","blog_title":"RevComm Tech Blog","published":"2026-06-04 12:00:00","type":"rich","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Ftech.revcomm.co.jp%2Fintroduction-to-grpo\" title=\"Introduction to GRPO and Its Variants - RevComm Tech Blog\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","version":"1.0","provider_url":"https://hatena.blog","description":"Background The release of DeepSeekMath[1] and DeepSeek-R1[2] brought Group Relative Policy Optimization (GRPO) into the spotlight, and it quickly became one of the most widely adopted post-training algorithms in the open-source LLM community. GRPO's significance lies in making Reinforcement Learning\u2026","title":"Introduction to GRPO and Its Variants","height":"190","blog_url":"https://tech.revcomm.co.jp/","categories":["Research","\u751f\u6210AI"]}