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Protein language models, like LLMs, suffer from a "Curse of Depth," where deeper layers contribute surprisingly little to the final prediction, suggesting opportunities for more efficient architectures.
Looping and depth-growing, two distinct methods for improving LLM reasoning, are actually two sides of the same iterative computation coin, and can be combined for even better results.