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In this work we theoretically show that conservative objective models (COMs)for offline model-based optimisation (MBO) are a special kind of contrastivedivergence-based energy model, one where the energy function represents boththe unconditional probability of the input and the conditional probability ofthe reward variable. While the initial formulation only samples modes from itslearned distribution, we propose a simple fix that replaces its gradient ascentsampler with a Langevin MCMC sampler. This gives rise to a specialprobabilistic model where the probability of sampling an input is proportionalto its predicted reward. Lastly, we show that better samples can be obtained ifthe model is decoupled so that the unconditional and conditional probabilitiesare modelled separately.

Conservative objective models are a special kind of contrastive divergence-based energy model