Reinforcement learning training pipelines are hindered by the rollout stage, which can be accelerated using Multi-Token Prediction (MTP) with speculative decoding. However, MTP acceptance rates decline significantly during training, limiting its potential to speed up rollouts. Researchers have proposed a method to break entropy bounds and accelerate RL training via MTP with rejection sampling, addressing the acceptance rate issue1. This approach enables more efficient training of large language models, which rely heavily on RL. The security implications of these developments are substantial, as they can reshape both capability and risk surfaces. As large language models become more prevalent, the potential risks and benefits associated with their development must be carefully considered. The acceleration of RL training has significant consequences for the security community, as it can lead to more sophisticated and potentially vulnerable models, so what matters most is understanding the security implications of these advancements to mitigate potential risks.