TTPO: Test-Time Policy Optimization

Enables label-free test-time training by replacing ground-truth labels with majority-vote pseudo-labels and exploiting the asymmetric failure of that substitution: rollouts agreeing with the pseudo-label are distilled via on-policy self-distillation, while disagreeing rollouts are penalized via grouped RL, with token-level selection on both branches. Matches label-supervised OPSD on five competition-level math benchmarks, lifting Qwen3-1.7B from 38.0% to 45.2% in test-time training and +25-36% in no-thinking settings.