OptPO: Optimal Rollout Allocation for Test-time Policy Optimization

• URL: http://arxiv.org/abs/2512.02882v1
• Date: Tue, 02 Dec 2025 15:38:52 GMT
• Title: OptPO: Optimal Rollout Allocation for Test-time Policy Optimization
• Authors: Youkang Wang, Jian Wang, Rubing Chen, Tianyi Zeng, Xiao-Yong Wei, Qing Li,
• Abstract summary: Test-time policy optimization enables large language models to adapt to distribution shifts by leveraging feedback from self-generated rollouts.<n>We propose Optimal Rollout Allocation for Test-time Policy Optimization (OptPO), a principled framework that adaptively allocates inference budgets.
• Score: 11.375209834858135
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Test-time policy optimization enables large language models (LLMs) to adapt to distribution shifts by leveraging feedback from self-generated rollouts. However, existing methods rely on fixed-budget majority voting to estimate rewards, incurring substantial computational redundancy. We propose Optimal Rollout Allocation for Test-time Policy Optimization (OptPO), a principled framework that adaptively allocates inference budgets. By formulating the voting process as a Bayesian sequential probability ratio test, OptPO dynamically halts sampling once the posterior confidence in a consensus answer exceeds a specified threshold. Crucially, it utilizes the retained rollouts for on-policy updates, seamlessly integrating with algorithms like PPO or GRPO without requiring ground-truth labels. Across diverse reasoning benchmarks, OptPO significantly reduces rollout overhead compared to fixed-sample baselines while preserving or improving accuracy. By unifying statistically optimal stopping with test-time learning, OptPO offers a computationally efficient paradigm for test-time adaptation. The source code will be open upon acceptance at https://open-upon-acceptance.

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