Multiverse: Your Language Models Secretly Decide How to Parallelize and Merge Generation
Xinyu Yang, Yuwei An, Hongyi Liu, Tianqi Chen, Beidi Chen
Autoregressive Large Language Models (AR-LLMs) frequently exhibit implicit parallelism in sequential generation. Inspired by this, we introduce **Multiverse**, a new generative model that enables natively parallel generation. Multiverse internalizes a MapReduce paradigm, generating automatically through three stages: (i) a *Map* stage for adaptive task decomposition, (ii) a *Process* stage for parallel subtask execution, and (iii) a *Reduce* stage for lossless result synthesis. Next, we build a real-world Multiverse reasoning model with co-design of data, algorithm, and system, enabling rapid and seamless transfer from frontier AR-LLMs. Starting from sequential reasoning chains, we create **Multiverse 1K** by converting them into structured training data using an automated LLM-assisted pipeline, avoiding costly human annotations. Algorithmically, we design **Multiverse Attention** to separate parallel reasoning steps while keeping compatibility with causal attention for efficient training. Systematically, we implement **Multiverse Engine** to enable parallel inference. It features a dedicated scheduler that dynamically switches between sequential and parallel generation, triggered directly by the model. After a 3-hour fine-tuning with 1K examples, *our Multiverse-32B stands as the only non-AR model achieving performance on par with leading AR-LLMs of the same scale*, evidenced by AIME24 & 25 scores of 52% and 43%, respectively. Moreover, our budget control experiments show that Multiverse-3B exhibits superior scaling, outperforming AR-LLMs by 1.87% on average using the same context length. Such parallel scaling further leads to practical efficiency gain, achieving up to 2$\times$ speedup across varying batch sizes.