Microsoft Paper Tests Reusing Rules from Past Agent Runs

Rohan Paul shared details from a new Microsoft paper on AI agent reasoning. The paper suggests collecting 35 to 50 past trajectories to learn a compact set of rules. These rules could substitute for expensive test-time reasoning in subsequent tasks. The post poses the question of paying the reasoning cost once and reusing the model's learned insights across multiple future tasks. It describes testing this cheaper alternative for agent runs. The tweet includes a static screenshot of the research paper as its attachment.