Reusable rules could replace some costly AI reasoning, a post says
The post describes a Microsoft paper that extracts recurring failure patterns from 35–50 past agent runs, then turns them into a small Markdown “skill” added to a non-reasoning model’s system prompt.
TLDR
According to a post describing Microsoft research, reusable skills helped GPT-5.4-mini recover 55%–100%+ of the gap between non-reasoning and reasoning modes across four agent benchmarks, while using 2.9–4.5 times fewer output tokens than reasoning. The post says the skilled non-reasoning model beat reasoning mode on ALFWorld and τ²-retail, but reasoning still won on telecom and SpreadsheetBench. It also says skills built only from cheap non-reasoning runs were competitive across all four domains—the method did not require reasoning traces.
Combined views
—
2 Sources, first seen ago
