Train 'n Trade: Foundations of Parameter Markets
Tzu-Heng Huang Harit Vishwakarma Frederic Sala
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.