Core Viewpoint - The article introduces a novel reasoning paradigm called "Diffusion Chain of Lateral Thought," which enhances the reasoning capabilities of large models by treating intermediate results in the reverse diffusion process as steps in the reasoning process, optimizing the final output's correctness through reinforcement learning [1][34]. Group 1: Introduction of the Concept - The "Diffusion Chain of Lateral Thought" is a new reasoning paradigm proposed by Professor Qi Guojun's team at West Lake University MAPLE Lab, emphasizing the importance of divergent thinking in large model training and inference [1][6]. - This method allows for non-linear generation of responses, contrasting with traditional linear reasoning chains, thereby encouraging more creative and exploratory reasoning paths [1][7]. Group 2: Application and Results - The method has been successfully applied to two representative diffusion language models, showing significant improvements in mathematical reasoning and code generation tasks, surpassing existing models [2][30]. - The team trained the "Ordered Mask Generation Diffusion Language Model" (LLaDOU) based on the LLaDA model, achieving superior performance in complex reasoning tasks compared to other diffusion language models [2][31]. Group 3: Experimental Validation - Experiments demonstrated that the DCoLT approach outperformed traditional methods like Chain of Thought (CoT) and DoT in tasks such as Sudoku solving and mathematical reasoning, achieving a 57.0% accuracy on the GSM8K-Aug dataset [30]. - The LLaDOU model achieved an accuracy of 88.1% in mathematical reasoning tasks, significantly higher than other models, indicating the effectiveness of the proposed reasoning paradigm [32]. Group 4: Theoretical Implications - The research highlights that traditional autoregressive models are not the only choice for generating answers, suggesting that optimizing the order of token generation can lead to more effective reasoning processes [2][34]. - The findings provide important insights into the training and inference of foundational large models, advocating for a shift from linear to non-linear reasoning paradigms in AI [2][6].