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Enhancing Language Models with Relaxed Recursive Transformers
This chapter explores advanced techniques for improving small-scale language models through Relaxed Recursive Transformers developed by Google. It discusses the effective sharing of parameters, memory efficiency, and the stepwise method for optimizing transformer architectures. The implications for AI interpretation and protein modeling are also examined, highlighting the role of innovative approaches like sparse autoencoders.