You can now search for efficient neural architectures on a standard consumer GPU by combining RL-trained Transformers with swarm optimization, discovering models 10-100x smaller than standard baselines while maintaining competitive accuracy.
This paper presents a hybrid Neural Architecture Search (NAS) framework combining a Transformer-based reinforcement learning controller with an Artificial Bee Colony algorithm to design efficient deep learning models on consumer GPUs.