From Imitation to Reasoning: How LLMs Evolved Beyond Simple Instruction Tuning

Major AI models like GPT-4.5, DeepSeek-V3, and Claude 3.5 Sonnet were the last flagship systems built entirely on the traditional pretrain-then-instruct approach, where all computation occurred in a single forward pass with no revision mechanisms. By late 2024, AI labs hit a scaling ceiling where adding more data, parameters, or compute no longer yielded proportional gains, especially on complex multi-step reasoning tasks. This prompted a shift toward training models to 'think before they answer' using reinforcement learning techniques layered on top of existing training stages. Supervised fine-tuning (SFT), which teaches models by imitating human-annotated examples, proved limited because model quality was capped by annotator skill and offered no mechanism for exploring better alternatives. Reinforcement learning methods like RLHF addressed this by allowing models to generate multiple candidate responses and receive feedback signals indicating which outputs were actually superior.