# MENTOR

Reinforcement learning from verifiable rewards (RLVR) can stall when a jump in task difficulty makes every sampled trajectory fail, leaving zero reward and no learning signal. MENTOR keeps those failed trajectories, has a set of agents diagnose the error in real time and write a targeted instructional hint, then prepends the hint to the original problem for a new rollout. On math and code generation it beats GRPO, DAPO and static hint libraries, with average relative math gains up to 78.3% on Llama-3.2-3B-Instruct.

Reinforcement learning from verifiable rewards (RLVR) can stall when a jump in task difficulty makes every sampled trajectory fail, leaving zero reward and no learning signal. MENTOR keeps those failed trajectories, has a set of agents diagnose the error in real time and write a targeted instructional hint, then prepends the hint to the original problem for a new rollout. On math and code generation it beats GRPO, DAPO and static hint libraries, with average relative math gains up to 78.3% on Llama-3.2-3B-Instruct.