Single Transformer Layer Rivals Full RL Models, Suggests Simpler Architecture Path

A new paper argues that one transformer layer can match the performance of full-parameter reinforcement learning models, challenging assumptions about architecture depth. For developers building RL systems or exploring model compression, this finding could mean simpler, faster inference without sacrificing capability.