将扩散模型与MCTS结合，让规划过程随计算量提升而持续优化。
Monte Carlo Tree Diffusion for System 2 Planning
- 把去噪过程变成树状结构，逐步评估和优化计划路径。
- 在长时序任务中优于传统扩散模型，计算量越大效果越明显。
- 适合需要高精度、长程规划的智能系统，如机器人控制。
扩散模型近年来成为规划的强大工具。然而，与蒙特卡洛树搜索（MCTS）——其性能随推理时间计算量增加而自然提升——不同，基于扩散的规划器仅提供有限的可扩展性。本文提出蒙特卡洛树扩散（MCTD），一种将扩散模型生成能力与MCTS自适应搜索优势相结合的新框架。我们的方法重新构想去噪为树状结构过程，使部分去噪的计划可被迭代评估、剪枝与优化。通过有选择地扩展有前景的轨迹，同时保留对次优分支的回溯与改进能力，MCTD在扩散框架内实现了类似MCTS的探索-利用权衡控制。在具有挑战性的长时序任务上的实验证明，随着推理时间计算量增加，MCTD优于扩散基线，生成更高质量的解决方案。
原文摘要 · Abstract (English)
Diffusion models have recently emerged as a powerful tool for planning. However, unlike Monte Carlo Tree Search (MCTS)-whose performance naturally improves with inference-time computation scaling-standard diffusion-based planners offer only limited avenues for the scalability. In this paper, we introduce Monte Carlo Tree Diffusion (MCTD), a novel framework that integrates the generative strength of diffusion models with the adaptive search capabilities of MCTS. Our method reconceptualizes denoising as a tree-structured process, allowing partially denoised plans to be iteratively evaluated, pruned, and refined. By selectively expanding promising trajectories while retaining the flexibility to revisit and improve suboptimal branches, MCTD achieves the benefits of MCTS such as controlling exploration-exploitation trade-offs within the diffusion framework. Empirical results on challenging long-horizon tasks show that MCTD outperforms diffusion baselines, yielding higher-quality solutions as inference-time computation increases.
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