Researchers have made a breakthrough in training reasoning models using the Tsallis loss continuum, which enables more efficient adaptation to new tasks with limited supervision. When initial success probabilities are low, traditional reinforcement learning from verifiable rewards (RLVR) often falters, but the Tsallis loss family offers a solution by interpolating between RLVR and log-marginal-likelihood over latent trajectories. This approach allows for a more nuanced balance between exploration and exploitation, with the Tsallis $q$-logarithm controlling the trade-off. By adjusting the $q$ parameter, models can transition from RLVR to a more probabilistic framework, enhancing their ability to learn from sparse rewards1. This advancement has significant implications for state-aligned activity involving reinforcement learning, as it shifts the threat model from criminal to geopolitical, requiring a distinct response strategy. So what matters to practitioners is that this new methodology can be leveraged to develop more effective and adaptable models, ultimately informing a revised playbook for geopolitical threats.