Reinforcement Learning Is Rewriting the Rules of AI‑Driven Code
Reinforcement learning (RL) is moving beyond LeetCode puzzles into the messy, stateful world of real software development. By leveraging massive offline GitHub histories, decomposing tasks into atomic skills, and training models on execution traces, researchers are building agents that can navigate file systems, run tests, and understand runtime state. The result is a new class of verifiable coding assistants that learn from human work rather than brute‑force trial and error.