Dialog agents rely on instruction tuning and RLHF across major models (ChatGPT, InstructGPT, LaMDA, Sparrow, Claude)

AI Impact Summary

The article surveys the landscape of language-model dialog agents and centers on instruction-following as the core driver of usefulness, highlighting IFT, SFT, RLHF, and CoT as the practical levers. It compares major models (ChatGPT, InstructGPT, LaMDA, BlenderBot 3, Sparrow, Claude/Assistant) and notes how data strategy and safety rules shape outcomes. For a technical team, the implication is to scrutinize instruction-tuning and alignment pipelines, plan cross-model evaluation, and invest in high-quality instruction data to sustain dialog usefulness and safety across deployments.