Microsoft paper reveals efficient skill distillation for GPT-5.4-mini

Microsoft researchers have found a way to make a cheaper AI model outperform its own expensive reasoning mode, and the trick costs about as much as a latte. A paper published on August 11, titled “Reason Wide, Not Deep: Amortizing the Reasoning Premium into Distilled Skills,” introduces a passive skill-distillation method that extracts compact rule sets from a small number of task examples. Those rules, written in plain markdown, get injected into a model’s system prompt and effectively teach it shortcuts that bypass the need for costly chain-of-thought reasoning. The target model in this case: GPT-5.4-mini, which launched back in March. How it works The process is deceptively simple. A coding agent reviews between 35 and 50 task trajectories, essentially recordings of how a model approached and sometimes failed at specific tasks. From those trajectories, the agent distills a natural-language “skill” document ranging from 40 to 130 lines of markdown. These aren’t abstract embeddings or fine-tuned weight adjustments. They’re readable, auditable rules derived from what went wrong and what went right. Once that skill document is folded into the system prompt of a non-reasoning model, ...