Native Parallel Reasoner: Reasoning in Parallelism via Self-Distilled Reinforcement Learning
We introduce Native Parallel Reasoner (NPR), a teacher-free framework that enables Large Language Models (LLMs) to self-evolve genuine parallel reasoning capabilities. NPR transforms the model from sequential emulation to native parallel cognition through three key innovations: 1) a self-distilled progressive training paradigm that transitions from ``cold-start'' format discovery to strict topological constraints without external supervision; 2) a novel Parallel-Aware Policy Optimization (PAPO) algorithm that optimizes branching policies directly within the execution graph, allowing the model to learn adaptive decomposition via trial and error; and 3) a robust NPR Engine that refactors memory management and flow control of SGLang to enable stable, large-scale parallel RL training. Across eight reasoning benchmarks, NPR trained on Qwen3-4B achieves performance gains of up to 24.5% and inference speedups up to 4.6x. Unlike prior baselines that often fall back to autoregressive decoding, NPR demonstrates 100% genuine parallel execution, establishing a new standard for self-evolving, efficient, and scalable agentic reasoning.
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Tong Wu
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2025-12-09T09:22:22.731Z
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victor wu
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Yang Liu
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2025-12-09T09:22:20.278Z
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Yang
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Jun Bai
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2025-12-09T09:22:17.404Z
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Jun Bai
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Zixia Jia
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Shuyi Zhang
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Ziyong Lin
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Yanting Wang
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2025-12-09T09:22:14.418Z
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YANTING WANG
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Song-Chun Zhu
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Zilong Zheng
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2025-12-10T09:10:05.315Z
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Zilong Zheng
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Native Parallel Reasoner: Reasoning in Parallelism via Self-Distilled Reinforcement Learning
We introduce Native Parallel Reasoner (NPR), a teacher-free framework that enables Large Language Models (LLMs) to self-evolve genuine parallel reasoning capabilities. NPR transforms the model from sequential emulation to native parallel cognition through three key innovations: 1) a self-distilled progressive training paradigm that transitions from ``cold-start'' format discovery to strict topological constraints without external supervision; 2) a novel Parallel-Aware Policy Optimization (PAPO) algorithm that optimizes branching policies directly within the execution graph, allowing the model to learn adaptive decomposition via trial and error; and 3) a robust NPR Engine that refactors memory management and flow control of SGLang to enable stable, large-scale parallel RL training. Across eight reasoning benchmarks, NPR trained on Qwen3-4B achieves performance gains of up to 24.5% and inference speedups up to 4.6x. Unlike prior baselines that often fall back to autoregressive decoding, NPR demonstrates 100% genuine parallel execution, establishing a new standard for self-evolving, efficient, and scalable agentic reasoning.
NPR, a teacher-free framework, enhances Large Language Models with native parallel reasoning capabilities through self-distilled training, Parallel-Aware Policy Optimization, and a robust NPR Engine, achieving substantial performance and speed improvements.
Native Parallel Reasoner Large Language Models self-evolve parallel reasoning self-distilled progressive training cold-start format discovery topological constraints Parallel-Aware Policy Optimization branching policies execution graph adaptive decomposition trial and error NPR Engine memory management flow control parallel RL training reasoning benchmarks Qwen3-4B genuine parallel execution autoregressive decoding agentic reasoning