Single Transformer Layer Matches Full-Parameter RL Training

Researchers found that a one-layer transformer can match the performance of full-parameter models in reinforcement learning tasks, challenging assumptions about depth requirements. For indie ML practitioners, this suggests potential efficiency gains—simpler architectures might replace bloated models for certain RL problems, reducing computational overhead and deployment costs.