Major advances in Machine Learning (ML) and Artificial Intelligence (AI) increasingly take the form of developing and releasing general-purpose models. These models are designed to be adapted by other businesses and agencies to perform a particular, domain-specific function. This process has become known as adaptation or fine-tuning. This paper offers a model of the fine-tuning process where a Generalist brings the technological product (here an ML model) to a certain level of performance, and one or more Domain-specialist(s) adapts it for use in a particular domain. Both entities are profit-seeking and incur costs when they invest in the technology, and they must reach a bargaining agreement on how to share the revenue for the technology to reach the market. For a relatively general class of cost and revenue functions, we characterize the conditions under which the fine-tuning game yields a profit-sharing solution. We observe that any potential domain-specialization will either contribute, free-ride, or abstain in their uptake of the technology, and we provide conditions yielding these different strategies. We show how methods based on bargaining solutions and sub-game perfect equilibria provide insights into the strategic behavior of firms in these types of interactions, and we find that profit-sharing can still arise even when one firm has significantly higher costs than another. We also provide methods for identifying Pareto-optimal bargaining arrangements for a general set of utility functions.

翻译：机器学习与人工智能的重大进展日益体现为通用模型的开发与发布，这些模型旨在由其他企业或机构适配至特定领域功能，这一过程被称为适配或微调。本文提出一个微调过程模型：通才方将技术产品（即机器学习模型）提升至一定性能水平，而一个或多个领域专家方将其适配至特定应用场景。双方均追求利润，并在技术投入中承担成本，同时必须就技术进入市场后的收益分配达成议价协议。在一类较为通用的成本与收益函数框架下，我们刻画了微调博弈产生利润共享方案的条件。观察到任何潜在的领域专长方在技术采用中可能选择三种策略——贡献、搭便车或放弃，并给出了这些策略的生成条件。我们证明，基于议价解和子博弈完美均衡的方法能揭示企业在此类互动中的战略行为，并发现即使某一方的成本显著高于另一方，利润共享仍然可能实现。此外，我们还提供了在通用效用函数集合中识别帕累托最优议价安排的方法。