用LoRA微调大模型，让AI在万智牌中实时推荐抽卡决策。
UrzaGPT: LoRA-Tuned Large Language Models for Card Selection in Collectible Card Games
- 用标注的抽卡日志对LLM进行LoRA微调，适配万智牌场景。
- 微调后小模型准确率达66.2%，远超零样本的43%。
- 方法通用可更新，适合快速适配新卡包的玩家和开发者。
集换式卡牌游戏（CCGs）因部分可观测性、长期决策与不断更新的卡池，对AI构成挑战，现有模型在套牌构建和玩法上远逊于人类。本文提出UrzaGPT，一种针对万智牌实时光环抽卡决策的领域自适应大语言模型。基于开源权重的LLM，我们在标注的抽卡日志数据集上采用低秩适应（LoRA）微调，利用大模型的语言建模能力，实现对新扩展包的快速适配。实验表明，未微调的小模型（如Llama-3-8B）完全无法抽卡，而大模型GPT-4o在零样本下准确率为43%。使用UrzaGPT微调小模型仅需10,000步，即达66.2%准确率。虽尚未超越专用模型，但证明仅用大模型即可实现有效抽卡，未来有望构建高性能、通用且易更新的自动抽卡AI。
原文摘要 · Abstract (English)
Collectible card games (CCGs) are a difficult genre for AI due to their partial observability, long-term decision-making, and evolving card sets. Due to this, current AI models perform vastly worse than human players at CCG tasks such as deckbuilding and gameplay. In this work, we introduce UrzaGPT, a domain-adapted large language model that recommends real-time drafting decisions in Magic: The Gathering. Starting from an open-weight LLM, we use Low-Rank Adaptation fine-tuning on a dataset of annotated draft logs. With this, we leverage the language modeling capabilities of LLM, and can quickly adapt to different expansions of the game. We benchmark UrzaGPT in comparison to zero-shot LLMs and the state-of-the-art domain-specific model. Untuned, small LLMs like Llama-3-8B are completely unable to draft, but the larger GPT-4o achieves a zero-shot performance of 43%. Using UrzaGPT to fine-tune smaller models, we achieve an accuracy of 66.2% using only 10,000 steps. Despite this not reaching the capability of domain-specific models, we show that solely using LLMs to draft is possible and conclude that using LLMs can enable performant, general, and update-friendly drafting AIs in the future.
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