Send us a text How Self-Improving Agents Are Learning to Code Without Human Data In this episode of the Colaberry AI Podcast, we explore a groundbreaking research framework called Self-play SWE-RL (SSR), which proposes a radically new way to train superintelligent software engineering agents—without relying on human-curated datasets, test suites, or natural language instructions. Instead of learning from static examples, SSR uses a self-play loop involving two autonomous agents. A bug-injecti...