open-loop

Introduces PIRL (Policy Improvement Reinforcement Learning) and its practical implementation PIPO, a closed-loop framework that verifies policy updates by comparing performance with a historical anchor, enabling correction or reinforcement of previous updates. Experiments show consistent gains in mathematical reasoning, code generation, tool use, and self-distillation when applied on top of existing RL algorithms like PPO and GRPO.