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The recent introduction of OpenAI’s O1/O3 model represents a significant milestone in developing strong reasoning capabilities in Large Language Models (LLMs). By introducing more computational budget during test-time, LLMs have the potential to explore more accurate and higher-quality solutions. However, such paradigms are primarily verified in domains that have well-defined criteria for responses, such as coding and mathematics. Inspired by the success of this paradigm, we aim to bridge it to more subtle open-domain question answering. Specifically, we utilize search mechanisms such as Monte Carlo Tree Search (MCTS) for both policy model improvement and reward model improvement that achieve better performance in test-time scaling strategies. Our contributions are summarized in two folds: For the training phase, we demonstrate that our approach surpasses previous SOTA automatic data annotation methods and various public instruction-tuning datasets, with fewer data points. This offers a more data-efficient solution for training robust models. For the inference phase, we utilize the intermediate values collected during training data construction to train a process reward model called PRM+. This model employs a novel two-stage training method to provide finer-grained guidance across the generation trajectory. This introduces no additional overhead during training data collection and further enhances performance by scaling test-time computation. Experimental results show that our method can effectively improve the performance of both the policy model and the reward model.