用自动验证生成高质量空间推理数据，助力小模型高效学习。
SpatialTraceGen: High-Fidelity Traces for Efficient VLM Spatial Reasoning Distillation
- 通过自动验证器生成多步、多工具的高保真推理链。
- 在CLEVR-Humans上提升推理质量17%，方差降低超40%。
- 适合想让小模型掌握复杂空间推理的研究者使用。
尽管视觉语言模型在诸多任务中表现优异，但在需要问题分解与策略性工具使用的复杂空间推理上仍显不足。微调小型可部署模型是实现高性能的有效路径，但受限于高质量、分步推理数据的缺乏。为此，我们提出SpatialTraceGen框架，将大模型的推理过程蒸馏为高质量的多跳、多工具推理轨迹数据集。其核心创新是自动化验证器，可规模化保证每一步推理的准确性，替代昂贵的人工标注。在CLEVR-Humans基准测试中，该验证器引导流程使轨迹平均质量评分提升17%，质量方差下降超过40%。SpatialTraceGen生成专家级推理轨迹数据，为小模型微调和样本高效的离线强化学习提供结构化、分步示例。
原文摘要 · Abstract (English)
While Vision-Language Models (VLMs) excel in many areas, they struggle with complex spatial reasoning, which requires problem decomposition and strategic tool use. Fine-tuning smaller, more deployable models offers an efficient path to strong performance, but this is hampered by a major bottleneck: the absence of high-quality, step-by-step reasoning data. To address this data-efficiency gap, we introduce SpatialTraceGen, a framework to distill the reasoning processes of a large teacher model into a high-quality dataset of multi-hop, multi-tool reasoning traces. A key innovation is our automated Verifier, which scalably ensures the fidelity of each reasoning step, providing a cost-effective alternative to manual human annotation. On the CLEVR-Humans benchmark, this verifier-guided process improves the average quality score of traces by 17\% while reducing quality variance by over 40\%. SpatialTraceGen delivers a dataset of expert traces, providing the structured, step-by-step examples of tool use necessary for effective fine-tuning and sample-efficient offline reinforcement learning.
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