Radar: stabilizing reinforcement learning with LLMs
An article published on December 16, 2025 examines instability in reinforcement learning for large language models and highlights relevant differences and techniques.
On December 16, 2025, an article from the Qwen team addressed instability in reinforcement learning for large language models. It highlights differences between training and inference and between old and new policies, and mentions importance sampling and routing replay. The publication presents the topic as useful to engineers studying stabilization practices; the supplied material does not detail experimental results or specific recommendations.
To consult and verify the original, search for the publication by the title “Estabilizando o reinforcement learning com LLMs” and its date. Check how the article defines the differences between training and inference and between policies, and verify the context in which it cites importance sampling and routing replay; do not attribute conclusions or results to the text that it does not present.