PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning
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.
6965b990fc8c4ecc02c7f8df
Jingcheng Hu
claimed_verified
2026-01-16T10:34:23.867Z
625026b7d2d191ac43320c5e
https://cdn-avatars.huggingface.co/v1/production/uploads/625026b7d2d191ac43320c5e/2ExzHlZ-Bk8SQMyBjeY6N.jpeg
Jingcheng Hu
reign12
user
reign12
6965b990fc8c4ecc02c7f8e0
Yinmin Zhang
6965b990fc8c4ecc02c7f8e1
Shijie Shang
6965b990fc8c4ecc02c7f8e2
Xiaobo Yang
6965b990fc8c4ecc02c7f8e3
Yue Peng
6965b990fc8c4ecc02c7f8e4
Zhewei Huang
6965b990fc8c4ecc02c7f8e5
Hebin Zhou
6965b990fc8c4ecc02c7f8e6
Xin Wu
6965b990fc8c4ecc02c7f8e7
Jie Cheng
6965b990fc8c4ecc02c7f8e8
Fanqi Wan
6965b990fc8c4ecc02c7f8e9
Xiangwen Kong
6965b990fc8c4ecc02c7f8ea
Chengyuan Yao
6965b990fc8c4ecc02c7f8eb
Kaiwen Yan
6965b990fc8c4ecc02c7f8ec
Ailin Huang
6965b990fc8c4ecc02c7f8ed
Hongyu Zhou
6965b990fc8c4ecc02c7f8ee
Qi Han
6965b990fc8c4ecc02c7f8ef
Zheng Ge
6965b990fc8c4ecc02c7f8f0
Daxin Jiang
6965b990fc8c4ecc02c7f8f1
Xiangyu Zhang
6965b990fc8c4ecc02c7f8f2
Heung-Yeung Shum
PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning
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.
Parallel Coordinated Reasoning enables large-scale test-time compute scaling beyond sequential reasoning limitations through parallel exploration and message-passing architecture.
test-time compute sequential reasoning parallel exploration message-passing architecture reinforcement learning multi-million-token HMMT 2025 GPT-5