Researchers have made a significant discovery about the training of large language models (LLMs) using reinforcement learning with verifiable rewards (RLVR), finding that the weight trajectories of these models are extremely low-rank and highly predictable1. This means that only minimal RLVR training is required to achieve desirable outcomes, as the majority of downstream performance can be extrapolated from a limited set of parameters. The low-rank nature of these trajectories suggests that the underlying geometry of the model's parameter space is more structured than previously thought. This breakthrough has important implications for the development and deployment of LLMs, particularly in terms of their potential risks and security vulnerabilities. As LLMs become increasingly powerful and widespread, understanding the dynamics of their training processes is crucial for mitigating potential security threats, so this discovery matters to practitioners seeking to develop more secure and reliable AI systems.