New theory reveals coverage collapse risk in RLHF; policy regularization offers a fix

A year-long theory paper from Google DeepMind and collaborators shows that REINFORCE-style policy optimization can cause coverage collapse, driving optimal-response probability near zero and making RLHF convergence exponentially slow, while recent policy regularization approaches address the issue.

2026-11-11 ~ 2026-11-12 · 3 related posts

• Theory paper: REINFORCE suffers coverage collapse needing exponential steps — nanjiang_cs · 2026-11-11
• New RLHF Theory: Coverage Assumed Only at Initialization Causes Exponential Complexity Collapse — nanjiang_cs · 2026-11-11

1 near-duplicate retellings: gaotianyu1350