How does Claude's approach to instruction tuning differ from other LLMs?

17 Eyl 10:45

I'm trying to understand the underlying mechanics of Claude's instruction tuning pipeline. Specifically, how does the model incorporate human feedback during fine‑tuning, and what role do reinforcement learning steps play compared to pure supervised approaches? Also, are there any architectural constraints that make this process more efficient or limit its adaptability? Would love to hear thoughts or references that break down the process in a digestible way.