这篇论文解决了LLM推理的计算瓶颈，用消息传递代替长链条，在数独和SAT上效果显著，对关注推理效率的人值得一读。
论文提出Message Passing Language Models（MPLMs）框架，通过轻量级send/receive原语让多个LLM线程直接通信。在Sudoku任务中，MPLM所需的上下文比串行CoT和并行FJ方法渐近更小，并成功微调出能解25x25数独的单一模型。在3-SAT任务中，抢占机制可提前终止无效分支提升效率。在长上下文问答上，适当提示的大型预训练模型能遵循MPLM协议，性能与流行fork-join方法相当。
Message Passing Enables Efficient Reasoning
While inference-time scaling has improved the reasoning abilities of large language models (LLMs), the need to generate long chains-of-thought (CoTs) is a computational bottleneck. Thus, in contrast to sequential scaling methods like CoT, recent parallel scaling techniques instead use fork and join (FJ) primitives to divide work across multiple LLM threads. However, in the fork-join paradigm, threads are typically transient and do not communicate pointwise with one another which limits scalability. To tackle this, we introduce Message Passing Language Models (MPLMs), a framework for LLM reasoning in which threads communicate directly via lightweight send and receive primitives. MPLMs enable efficient scaling through two key mechanisms: (1) reduced communication costs, achieved by avoiding redundant context sharing, and (2) preemption, which allows threads to terminate early based on partial information from their peers. We demonstrate the promise of MPLMs on 3 classes of tasks. First, on Sudoku puzzles, we show that MPLMs require an asymptotically smaller context than both serial CoT and parallel FJ. We then fine-tune a single model to solve 25 x 25 puzzles that remain challenging for standard CoT and FJ approaches, as well as frontier reasoning models without tools. Second, on 3-SAT puzzles, the capability of preemption allows termination of unpromising branches, which results in improved efficiency. Finally, we show that appropriately prompted large pre-trained models follow the MPLM protocol, achieving competitive results on long-context question answering relative to popular fork-join approaches.