PIRL: From Open-Loop Exploration to Closed-Loop Reinforcement Learning [R]
<!-- SC_OFF --><div class="md"><p><strong>TL;DR:</strong> Most RL post-training algorithms optimize the current batch and move on. But after an update, did the new policy actually become better?</p> <p>We introduce <strong>Policy Improvement Reinforcement Learning (PIRL)</strong> and its practical implementation, <strong>Policy Improvement Policy Optimization (PIPO)</strong>—a plug-and-play framework that lets RL training look back, verify the previous update, and reinforce or correct it accordi
























