A new AI framework called MENTOR is proposed to address the issue of 'zero-reward collapse' in Reinforcement Learning from Verifiable Rewards (RLVR), a key driver for Large Reasoning Models. MENTOR dynamically generates targeted, instructional prompts to guide the learning agent's next attempt, improving efficiency and reducing training failures.