Organizations typically train large models individually. This is costly and time-consuming, particularly for large-scale foundation models. Such vertical production is known to be suboptimal. Inspired by this economic insight, we ask whether it is possible to leverage others' expertise by trading the constituent parts in models, i.e., sets of weights, as if they were market commodities. While recent advances in aligning and interpolating models suggest that doing so may be possible, a number of fundamental questions must be answered to create viable parameter markets. In this work, we address these basic questions, propose a framework containing the infrastructure necessary for market operations to take place, study strategies for exchanging parameters, and offer means for agents to monetize parameters. Excitingly, compared to agents who train siloed models from scratch, we show that it is possible to mutually gain by using the market, even in competitive settings. This suggests that the notion of parameter markets may be a useful paradigm for improving large-scale model training in the future.

翻译：组织通常独立训练大型模型，这一过程对于大规模基础模型而言成本高昂且耗时。这种纵向自产模式已知并非最优。受此经济学见解启发，我们提出疑问：是否可能通过交易模型中的组成部件（即权重集合）来借助他人专长，如同将其作为市场商品进行交易？尽管近期模型对齐与插值技术的进展表明此举具有潜在可行性，但构建可行的参数市场仍需解决若干基础性问题。本研究针对这些基本问题展开探讨，提出包含市场运行所需基础设施的框架，研究参数交换策略，并提供代理方通过参数实现货币化的方案。令人振奋的是，与从头训练孤立模型的代理方相比，即使在竞争性环境下，通过市场运作仍可实现互利共赢。这表明参数市场这一概念或将成为未来改进大规模模型训练的有效范式。