Researchers have introduced ExpRL, a novel approach to reinforcement learning for large language models (LLMs) that focuses on exploratory mid-training techniques. This method aims to enhance the reasoning capabilities of LLMs by leveraging sparse reward reinforcement learning, which has become a standard tool for improving model performance. However, the success of this approach relies heavily on the quality of the base model, particularly in terms of its coverage of useful primitive skills such as decomposition and self-correction1. By incorporating exploratory RL into the mid-training process, models can learn to navigate complex tasks and develop more effective problem-solving strategies. The development of ExpRL has significant implications for the security landscape, as advancements in LLMs can both expand their capabilities and increase their vulnerability to potential risks. As LLMs continue to evolve, understanding the security implications of these developments is crucial for mitigating potential threats.