用小模型生成补丁文本，让大模型更高效完成智能任务。
Supplement Generation Training for Enhancing Agentic Task Performance
- 小模型生成补充文本，附加到输入提升大模型表现
- 无需修改大模型即可动态适配新任务，性能显著提升
- 适合需要快速迭代的现实应用，降低部署成本
训练大型基础模型以执行智能体任务正变得日益不切实际，因其计算成本高、迭代周期长，且新模型不断发布导致快速过时。我们提出一种更高效可持续的策略——补丁生成训练（SGT）。该方法训练一个小型语言模型，生成有助于大模型完成任务的补充文本，将其附加至原始输入。这些轻量级模型可动态适应任务需求，在不修改大模型的前提下提升性能。该方法将特定任务优化与基础模型解耦，使大模型驱动的智能体在真实场景中实现更灵活、低成本的部署。
原文摘要 · Abstract (English)
Training large foundation models for agentic tasks is increasingly impractical due to the high computational costs, long iteration cycles, and rapid obsolescence as new models are continuously released. Instead of post-training massive models for every new task or domain, we propose Supplement Generation Training (SGT), a more efficient and sustainable strategy. SGT trains a smaller LLM to generate useful supplemental text that, when appended to the original input, helps the larger LLM solve the task more effectively. These lightweight models can dynamically adapt supplements to task requirements, improving performance without modifying the underlying large models. This approach decouples task-specific optimization from large foundation models and enables more flexible, cost-effective deployment of LLM-powered agents in real-world applications.
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