让离散推理模型自我纠错，动态调整计算量。
Self-Aware Markov Models for Discrete Reasoning
- 用自学习的马尔可夫转移核实现错误重掩码，支持自我修正。
- 在Sudoku-Extreme上达95%有效率，在Countdown-4平均10步解题成功率96%。
- 无需固定步骤数，适配复杂任务，可复用预训练模型微调。
标准掩码离散扩散模型在推理任务中受限于无法在掩码路径上自我修正，且依赖固定去噪步数，难以根据问题复杂度调整计算量。为此，本文提出一种基于自学习马尔可夫转移核的方法，该核基于自身输出进行训练，使标记可被重新掩码，从而实现对先前错误的修正。此外，不采用固定时间调度，而是使用训练得到的停止准则，使函数评估次数能随推理难度自适应调整。该方法仅增加两个轻量级预测头，便于复用和微调现有预训练模型。在Sudoku-Extreme数据集上，有效性达到95%；在Countdown-4任务中，平均仅需10步即可正确求解近96%的问题，部分题目甚至可在2步内完成。
原文摘要 · Abstract (English)
Standard masked discrete diffusion models face limitations in reasoning tasks due to their inability to correct their own mistakes on the masking path. Since they rely on a fixed number of denoising steps, they are unable to adjust their computation to the complexity of a given problem. To address these limitations, we introduce a method based on learning a Markov transition kernel that is trained on its own outputs. This design enables tokens to be remasked, allowing the model to correct its previous mistakes. Furthermore, we do not need a fixed time schedule but use a trained stopping criterion. This allows for adaptation of the number of function evaluations to the difficulty of the reasoning problem. Our adaptation adds two lightweight prediction heads, enabling reuse and fine-tuning of existing pretrained models. On the Sudoku-Extreme dataset we clearly outperform other flow based methods with a validity of 95%. For the Countdown-4 we only need in average of 10 steps to solve almost 96% of them correctly, while many problems can be solved already in 2 steps.
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