Parallel-R1: Reinforcement Learning for Parallel Thinking in LLMs

This September 10, 2025 technical report from Tencent AI Lab introduces Parallel-R1, a novel reinforcement learning (RL) framework designed to enhance large language models (LLMs) with parallel thinking capabilities for complex mathematical reasoning tasks. Unlike previous methods relying on supervised fine-tuning (SFT) over synthetic data, Parallel-R1 utilizes a progressive curriculum to address the cold-start problem in RL, initially using SFT on simpler tasks to instill the basic format of parallel thinking before transitioning to RL for exploration and generalization on more challenging problems. The research highlights that parallel thinking evolves from an exploratory strategy to a multi-perspective verification tool during training, and can also serve as a mid-training exploration scaffold to unlock higher performance ceilings. The framework demonstrated significant accuracy improvements across various math benchmarks, and the authors plan to open-source their model, data, and code.Source:https://arxiv.org/pdf/2509.07980

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