PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning

2026-01-09

arXiv.org

AI Summary

This work addresses the limitation of current language models, which are constrained by fixed context windows and struggle to effectively scale test-time compute (TTC). We propose Parallel Coordinated Reasoning (PaCoRe), a novel framework that introduces the first message-passing-based multi-round parallel reasoning architecture. By enabling parallel inference trajectories coupled with context-aware message compression and synthesis, PaCoRe extends effective TTC to millions of tokens without exceeding context length limits. The model is trained end-to-end via reinforcement learning to master information coordination and synthesis. Evaluated on the HMMT 2025 mathematical reasoning benchmark, our method achieves 94.5% accuracy, surpassing GPT-5 (93.2%). We publicly release the model, data, and full reasoning pipeline.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• test-time compute
• language models
• reasoning scalability
• context window
• sequential reasoning

Innovation

• Parallel Coordinated Reasoning
• Test-Time Compute
• Message-Passing Architecture
• Reinforcement Learning
• Scalable Inference