用扩散模型修正大模型推理错误，无需真实答案标签
DCGC: Draft-Conditioned Global Correction for Complex Reasoning with Masked Diffusion Models
- 基于草稿的全局修正框架，用扩散模型动态融合问题与草稿信息
- 在数学、代码等任务上显著提升准确率，尤其擅长修复低共识输出
- 无需真实错误标签，适合复杂推理中难以验证的场景
大语言模型的自回归生成容易将早期错误传递到后续推理。本文提出DCGC，一种基于掩码扩散模型（MDM）的全局修正框架，利用上游求解器生成的不完美草稿作为辅助上下文。DCGC结合任务特定的监督微调与一种新型推理时机制——动态双条件生成（Dynamic Dual-CFG），分离仅含问题和问题-草稿联合分支，并通过相对置信度差距缩放草稿相关残差。在数学、代码和知识推理基准上，DCGC优于标准采样和简单CFG变体，且结果显示其可迁移至不同扩散主干网络。在无真实失败标签的测试阶段，DCGC通过修正低共识的上游输出，提升了整个测试集的准确率，展现出作为无需验证器的全局修正模块在困难推理实例中的实用性。
原文摘要 · Abstract (English)
Correcting flawed reasoning traces remains a significant challenge for Large Language Models (LLMs), whose autoregressive generation can propagate early mistakes into subsequent reasoning. We introduce DCGC, a Masked Diffusion Model (MDM) framework for global correction that uses an imperfect solution draft from an upstream solver as auxiliary context. DCGC combines task-specific Supervised Fine-Tuning (SFT) with a novel inference-time mechanism called Dynamic Dual-CFG. This mechanism separates problem-only and joint problem-draft branches and scales the draft-conditioned residual using a relative confidence gap. Across math, code, and knowledge reasoning benchmarks, DCGC outperforms standard sampling and simpler CFG variants, with additional results suggesting transfer to different diffusion backbones. In test-time setting where ground-truth failure labels are unavailable, DCGC improves full test set accuracy by correcting low-consensus upstream outputs, highlighting its utility as a verifier-free global correction module for difficult reasoning instances.
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