用小模型实现星际争霸宏观管理，仅靠少量参数就表现良好。
SC-Phi2: A Fine-tuned Small Language Model for StarCraft II Macromanagement Tasks
- 用自监督学习微调Phi2模型，结合视觉信息生成动态指令。
- 在构建顺序和全局状态预测任务上达到优秀性能，参数量少。
- 支持单卡训练，适合资源有限的场景，适用于游戏AI研究者。
本文提出SC-Phi2，一个为星际争霸II宏观管理任务微调的小型语言模型。小型语言模型如Phi2、Gemma和DistilBERT是参数更少的大语言模型精简版，运行时对算力和内存需求更低。为让微软的Phi2模型掌握星际争霸知识，我们构建了一个包含种族、角色和动作信息的新文本数据集，并使用自监督学习对其进行微调。同时，将该语言模型与预训练的BLIP-2模型中的视觉变压器（ViT）结合，在MSC回放数据集上进行微调，从而生成包含视觉游戏状态信息的动态提示。不同于使用GPT-3.5等大模型的星际争霸语言模型，Phi2原本主要基于教科书数据训练，除本研究提供的训练内容外，缺乏对星际争霸的固有知识。通过采用LoRA（低秩适应）和量化技术，模型可在单张GPU上完成训练。实验表明，该模型在构建顺序和全局状态预测等微观管理任务中表现优异，且仅使用少量参数。
原文摘要 · Abstract (English)
This paper introduces SC-Phi2, a fine-tuned StarCraft II small language model for macromanagement tasks. Small language models, like Phi2, Gemma, and DistilBERT, are streamlined versions of large language models (LLMs) with fewer parameters that require less power and memory to run. To teach Microsoft's Phi2 model about StarCraft, we create a new SC2 text dataset with information about StarCraft races, roles, and actions and use it to fine-tune Phi-2 with self-supervised learning. We pair this language model with a Vision Transformer (ViT) from the pre-trained BLIP-2 (Bootstrapping Language Image Pre-training) model, fine-tuning it on the MSC replay dataset. This enables us to construct dynamic prompts that include visual game state information. Unlike the large models used in StarCraft LLMs such as GPT-3.5, Phi2 is trained primarily on textbook data and contains little inherent knowledge of StarCraft II beyond what is provided by our training process. By using LoRA (Low-rank Adaptation) and quantization, our model can be trained on a single GPU. We demonstrate that our model performs well at micromanagement tasks such as build order and global state prediction with a small number of parameters.
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