We propose a parallel reasoner, aiming at mitigating the hallucination and enhance fairness in reasoning.
Welcome to vote it on Hugging Face !
[Twitter Post] https://x.com/ZilongZheng/status/1998252267783516444
[Project Page & Demo] https://bigai-nlco.github.io/Native-Parallel-Reasoner
[GitHub Repo] https://github.com/bigai-nlco/Native-Parallel-Reasoner
[HF Paper] https://huggingface.co/papers/2512.07461
[arXiv Preprint] https://arxiv.org/abs/2512.07461
Highlight: This model is a native parallel reasoning system PoC that differs from other popular Multi-Agent approaches with multiple reasoning paths. Instead of having multiple agents, it has a single agent doing multi-path reasoning within the same time slice. Training starts from a single serial model with zero external supervision, using self-distillation to generate synthetic trajectories, then multi-stage optimization through imitation learning and RL + SGLang infrastructure adapted for parallel thinking acceleration. It thinks and solves problems using naive parallelism. With minimal self-distilled parallel reasoning trajectory samples, it matches and slightly exceeds existing parallel and autoregressive reasoning baselines on several math and complex reasoning benchmarks. On the inference side, it achieves up to 4.6x wall-clock speedup. Physically, it achieves ~100% parallel triggering. Logically, it exhibits emergent problem decomposition and divide-and-conquer capabilities, internalizing parallel thinking rather than falling back to serial strategies.