The Instruct Monomyth: why base models matter

This essay frames language modeling as a deep, twisty labyrinth. It cautions that simply scaling parameters and stacking instructions does not inherently produce better models. Instead, the author argues that the instruct paradigm can homogenize models, sacrificing the richness of base models for superficial alignment. The piece calls for exploration of alternative architectures and a return to focusing on strong base models rather than blind scaling. It invites readers to question the assumption that bigger means better and to respect the complexity of language and cognition when building future systems.