Research reveals that a single transformer layer can achieve comparable results to full-parameter reinforcement learning training, challenging conventional approaches that update all model parameters uniformly. This finding suggests that not all layers contribute equally to the gains obtained during post-training, prompting a reevaluation of the role of individual layers in reinforcement learning. By training a single layer, researchers can potentially match the performance of full-parameter training, which has significant implications for the development of large language models. The study's results indicate that the distribution of reinforcement learning adaptation across transformer layers is not uniform, with some layers playing a more crucial role than others1. This discovery has far-reaching consequences, particularly in the context of state-aligned activity involving transformer models, where the threat model shifts from criminal to geopolitical, requiring a different set of strategies and countermeasures. So what matters to practitioners is that this finding can inform the design of more efficient and effective reinforcement learning algorithms.