From the RL perspective, it is extremely challenging to derive a precise rewardfunction forhuman preferences since thecommands areabstract and the valid behaviors are highly complicated and multi-modal.

Recent works proposed various linear self-attention mechanisms, scaling only asO(L)for serial computation. We conduct a thorough complexity analysis of Performers,aclass which includes most recent linear Transformer mechanisms.

In a setting with multiple buyers, a buyer's expected value for an experiment may