Researchers have identified a critical limitation in Group Relative Policy Optimization (GRPO), a method used to tackle complex problems. When a model encounters its most challenging tasks, the lack of successful rollouts in a group leads to vanishing advantages, resulting in wasted examples that are crucial for learning. To address this issue, a novel approach called Adaptive Trace Prefix Control has been proposed, which involves prepending a correct prefix of a reference solution to raise the success rate. This technique allows for continuous control over problem difficulty by adjusting the prefix length. The implications of this research extend beyond the realm of machine learning, as state-aligned threat activity can elevate the stakes from criminal to geopolitical, affecting not only the immediate target but also the broader landscape1. This development matters to practitioners because it can significantly impact the effectiveness of GRPO in solving hard reasoning problems, potentially leading to breakthroughs in various fields.