Outcome-Guided On-Policy Self-Distillation
On-policy self-distillation (OPSD) provides denser token-level supervision and better computational efficiency than Reinforcement Learning with Verifiable Rewards (RLVR). However, this denser supervision may introduce substantial noise and training instability. Existing improvements often rely on high-variance per-toke...
Zhe-Xu Wang, Mao-Lin Luo, Yan-Kun Hong et al.
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