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  "text": "1. Native Parallel Reasoner: Reasoning in Parallelism via Self-Distilled Reinforcement Learning\n\n🔑 Keywords: NPR, Large Language Models, self-distilled training, Parallel-Aware Policy Optimization, genuine parallel reasoning  \n\n💡 Category: Knowledge Representation and Reasoning  \n\n🌟 Research Objective:  \n   - The paper introduces NPR, a teacher-free framework that enhances Large Language Models (LLMs) with native parallel reasoning capabilities.  \n\n🛠️ Research Methods:  \n   - NPR employs a self-distilled progressive training paradigm, Parallel-Aware Policy Optimization (PAPO) algorithm, and a robust NPR Engine to enable native parallel cognition without external supervision.  \n\n💬 Research Conclusions:  \n   - Trained on Qwen3-4B, NPR achieves performance gains of up to 24.5% and inference speedups up to 4.6x, setting a new standard for efficient and scalable agentic reasoning with 100% genuine parallel execution.  \n\n👉 Paper link: https://t.co/La4uXPrBrA",
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