给扩散语言模型的生成过程加了个重要性调控开关，让关键词更可控。
GIFT: Guided Importance-Aware Fine-Tuning for Diffusion Language Models
- 按词熵分配重要性权重，指导扩散过程中的生成方向。
- 在四个推理任务上，比传统微调方法平均提升6.2%以上。
- 适用于小数据、低参数量场景，适合做轻量化智能助手。
扩散模型在语言建模中展现出超越传统自回归方法的生成速度优势。然而，将监督微调（SFT）应用于扩散模型仍具挑战，因其在去噪步骤中缺乏精确的概率估计。尽管扩散机制支持对完整序列进行推理，但也使生成过程难以预测且常不一致，凸显了控制关键生成引导词的重要性。为此，我们提出GIFT，一种基于重要性感知的扩散语言模型微调方法，根据词的熵为不同词分配不同的重要性权重。该方法源自扩散理论，在多种设置下均表现优异：涵盖1k至10k规模的主流训练数据集，采用LoRA或全参数微调，针对基础或指令模型训练。在四个广泛使用的推理基准（Sudoku、Countdown、GSM8K、MATH-500）上，GIFT始终优于标准SFT，整体性能显著提升。
原文摘要 · Abstract (English)
Diffusion models have recently shown strong potential in language modeling, offering faster generation compared to traditional autoregressive approaches. However, applying supervised fine-tuning (SFT) to diffusion models remains challenging, as they lack precise probability estimates at each denoising step. While the diffusion mechanism enables the model to reason over entire sequences, it also makes the generation process less predictable and often inconsistent. This highlights the importance of controlling key tokens that guide the direction of generation. To address this issue, we propose GIFT, an importance-aware finetuning method for diffusion language models, where tokens are assigned different importance weights based on their entropy. Derived from diffusion theory, GIFT delivers substantial gains: across diverse settings including different mainstream training datasets ranging from 1k to 10k in size, utilizing LoRA or full parameter fine-tuning, and training on base or instruct models, GIFT consistently achieves superior overall performance compared to standard SFT on four widely used reasoning benchmarks (Sudoku, Countdown, GSM8K, and MATH-500).
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