PIRL/PIPO Adds Verification to RL Post-Training
机器之心 · wechat · 2026-07-12
Teams from Beihang University, Peking University, and Meituan have proposed PolicyImprovementReinforcementLearning (PIRL) and the actionable PIPO framework to address a long-overlooked issue in RL post-training:
- Traditional methods focus on "how to learn from the current batch of trajectories" without explicitly verifying if the policy actually improved after the update.
- PIRL treats "policy improvement" itself as the optimization target. PIPO adds a retrospective verification mechanism on top of existing methods like PPO, GRPO, DAPO, and self-distillation. It amplifies update directions that yield real gains while suppressing, offsetting, or correcting invalid or harmful updates.
The paper theoretically proves that for a fixed initial policy, maximizing cumulative policy improvement aligns with maximizing final policy performance. Experiments covering math reasoning, coding, tool calling, and self-distillation show that integrating PIPO improves both average performance and thought length across various base algorithms.
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