Terminal-RSI: Recursively Improving AI Agents on Terminal Tasks

Researchers introduced Terminal-RSI, a system designed to improve AI agents through repeated cycles of rewriting successful problem-solving strategies and refining decision-making policies. According to the researchers, the resulting 27B parameter model achieved an average success rate increase from 20.68% to 50.58% across four terminal task benchmarks. Open article →