DiffCoT: Diffusion-styled Chain-of-Thought Reasoning in LLMs
Chain-of-Thought (CoT) reasoning improves multi-step mathematical problem solving in large language models but remains vulnerable to exposure bias and error accumulation, as early mistakes propagate irreversibly through autoregressive decoding. In this work, we propose DiffCoT, a diffusion-styled CoT framework that reformulates CoT reasoning as an iterative denoising process. DiffCoT integrates diffusion principles at the reasoning-step level via a sliding-window mechanism, enabling unified generation and retrospective correction of intermediate steps while preserving token-level autoregression. To maintain causal consistency, we further introduce a causal diffusion noise schedule that respects the temporal structure of reasoning chains. Extensive experiments on three multi-step CoT reasoning benchmarks across diverse model backbones demonstrate that DiffCoT consistently outperforms existing CoT preference optimization methods, yielding improved robustness and error-correction capability in CoT reasoning.
695f278c5fa3847525c41d76
Shidong Cao
695f278c5fa3847525c41d77
Hongzhan Lin
admin_assigned
2026-01-09T16:50:31.411Z
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Hongzhan Lin
danielhzlin
user
danielhzlin
695f278c5fa3847525c41d78
Yuxuan Gu
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Ziyang Luo
admin_assigned
2026-01-09T16:50:42.777Z
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Ziyang Luo
Ziyang
user
Ziyang
695f278c5fa3847525c41d7a
Jing Ma
DiffCoT: Diffusion-styled Chain-of-Thought Reasoning in LLMs
Chain-of-Thought (CoT) reasoning improves multi-step mathematical problem solving in large language models but remains vulnerable to exposure bias and error accumulation, as early mistakes propagate irreversibly through autoregressive decoding. In this work, we propose DiffCoT, a diffusion-styled CoT framework that reformulates CoT reasoning as an iterative denoising process. DiffCoT integrates diffusion principles at the reasoning-step level via a sliding-window mechanism, enabling unified generation and retrospective correction of intermediate steps while preserving token-level autoregression. To maintain causal consistency, we further introduce a causal diffusion noise schedule that respects the temporal structure of reasoning chains. Extensive experiments on three multi-step CoT reasoning benchmarks across diverse model backbones demonstrate that DiffCoT consistently outperforms existing CoT preference optimization methods, yielding improved robustness and error-correction capability in CoT reasoning.
DiffCoT reformulates chain-of-thought reasoning as an iterative denoising process using diffusion principles, enabling unified generation and correction of intermediate steps while maintaining causal consistency.
chain-of-thought diffusion models denoising process autoregressive decoding exposure bias error accumulation sliding-window mechanism causal consistency causal diffusion noise schedule reasoning chains CoT preference optimization