Hao-Yu Zhang

When Sparse Reward Meets Dense Distillation: Training Dynamics of On-Policy Distillation

Reinforcement learning with verifiable rewards provides a sparse post-training signal: a single binary outcome evaluates the entire rollout, and every token receives the same sequence-level advantage regardless of its individual contribution. To complement this sparse supervision, a growing family of methods adds a sca...