%20outputs.%20Typically%2C%20preference%20optimization%20is%20approached%20as%20an%20offline%20supervised%20learning%20task%20using%20manually-crafted%20convex%20loss%20functions.%20While%20these%20methods%20are%20based%20on%20theoretical%20insights%2C%20they%20are%20inherently%20constrained%20by%20human%20creativity%2C%20so%20the%20large%20search%20space%20of%20possible%20loss%20functions%20remains%20under%20explored.%20We%20address%20this%20by%20performing%20LLM-driven%20objective%20discovery%20to%20automatically%20discover%20new%20state-of-the-art%20preference%20optimization%20algorithms%20without%20(expert)%20human%20intervention.%20Specifically%2C%20we%20iteratively%20prompt%20an%20LLM%20to%20propose%20and%20implement%20new%20preference%20optimization%20loss%20functions%20based%20on%20previously-evaluated%20performance%20metrics.%20This%20process%20leads%20to%20the%20discovery%20of%20previously-unknown%20and%20performant%20preference%20optimization%20algorithms.%20The%20best%20performing%20of%20these%20we%20call%20Discovered%20Preference%20Optimization%20(DiscoPOP)%2C%20a%20novel%20algorithm%20that%20adaptively%20blends%20logistic%20and%20exponential%20losses.%20Experiments%20demonstrate%20the%20state-of-the-art%20performance%20of%20DiscoPOP%20and%20its%20successful%20transfer%20to%20held-out%20tasks.)
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