Self-OPD: On-Policy Distillation for Flow Matching Models without Teacher

Removes the task-specific teacher from on-policy distillation of flow matching models by turning the student's own self-exploration into step-wise supervision: each deterministic next-state prediction branches into K stochastic SDE candidates scored against a self-reference baseline, with a pull-push objective attracting high-advantage branches and repelling low-advantage ones. Reports wins over prior RL and OPD methods on single and mixed reward benchmarks without any teacher.