Select and Improve: Understanding the Mechanics of Post-Training for Reasoning

This study investigates the mechanisms of post-training reinforcement learning (RL) for enhancing reasoning capabilities in models, specifically analyzing the Qwen-2.5-1.5B model. It identifies two primary mechanisms: strategy selection and strategy improvement, emphasizing the importance of supervised fine-tuning (SFT) data and the role of difficulty in RL data for activating these mechanisms. These findings offer valuable insights for practitioners aiming to scale reasoning capabilities in AI models through targeted training interventions.