Researchers at Hugging Face have published a retrospective on using agentic reinforcement learning (RL) for training GPT-OSS, an open-source version of the popular GPT model. Agentic RL involves training agents to make decisions that maximize rewards in complex environments. The authors provide practical advice and insights gained from their experience with GPT-OSS, including how to set up and tune agentic RL experiments. This work aims to help developers improve the performance and efficiency of large language models like GPT-OSS. --- Why it matters: This matters because agentic RL can significantly improve the training process for complex AI models like GPT-OSS, allowing researchers and developers to create more efficient and effective models. Source: https://huggingface.co/blog/LinkedIn/gpt-oss-agentic-rl