Group-Marginalized Self-Rewarding RL Drives Zero-Label Self-Evolving
GMAE aggregates reward realizations across possible contexts into a response-level distribution and estimates expected advantages, which exhibits stable learning, low extra cost, and good applicability across training datasets and RL backbones.
Yiming Wang, Yikang Liu, Qing-Yuan Tian et al.
· 0 citations