PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning
Authors: name: Jingcheng Hu, name: Yinmin Zhang, name: Shijie Shang, name: Xiaobo Yang, name: Yue Peng, name: Zhewei Huang, name: Hebin Zhou, name: Xin Wu, name: Jie Cheng, name: Fanqi Wan, name: Xiangwen Kong, name: Chengyuan Yao, name: Kaiwen Yan, name: Ailin Huang, name: Hongyu Zhou, name: Qi Han, name: Zheng Ge, name: Daxin Jiang, name: Xiangyu Zhang, name: Heung-Yeung Shum
Organization: StepFun

We introduce Parallel Coordinated Reasoning (PaCoRe), a training-and-inference framework designed to overcome a central limitation of contemporary language models: their inability to scale test-time compute (TTC) far beyond sequential reasoning under a fixed context window. PaCoRe departs from the traditional sequential paradigm by driving TTC through massive parallel exploration coordinated via a message-passing architecture in multiple rounds. Each round launches many parallel reasoning trajectories, compacts their findings into context-bounded messages, and synthesizes these messages to guide the next round and ultimately produce the final answer. Trained end-to-end with large-scale, outcome-based reinforcement learning, the model masters the synthesis abilities required by PaCoRe and scales to multi-million-token effective TTC without exceeding context limits. The approach yields strong improvements across diverse domains, and notably pushes reasoning beyond frontier systems in mathematics: an 8B model reaches 94.5% on HMMT 2025, surpassing GPT-5's 93.2% by scaling effective TTC to roughly two million tokens. We open-source model checkpoints, training data, and the full inference pipeline to accelerate follow-up work.