Study finds a single transformer layer can match full‑parameter RL training performance

A new arXiv paper investigates the efficiency of transformer architectures. The authors demonstrate that a single transformer layer can match full‑parameter RL training. Experiments were conducted on standard benchmark tasks. Results indicate comparable performance despite reduced model complexity. The study discusses implications for computational resource savings. It suggests potential for faster training cycles in research settings. The authors note limitations and propose further testing on larger datasets. Future work may explore scaling the approach to more complex environments.