Large Language Models often struggle with complex, multi-step reasoning where traditional Supervised Fine-Tuning (SFT) and Reinforcement Learning (RLVR) fail due to rigid imitation or sparse rewards. We dive into Supervised Reinforcement Learning (SRL), a novel framework that reformulates problem-solving into a sequence of logical actions, providing rich, step-wise guidance based on expert similarity. Discover how this approach enables small models to achieve superior performance in challenging mathematical reasoning and agentic software engineering tasks, inducing flexible and sophisticated planning behaviors.