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Topological Preference Optimization (TPO): A Novel Training Strategy for Conversational AI

We introduce **Topological Preference Optimization (TPO)**, a novel training methodology that leverages conversation topology and spatial-temporal coordinates to generate preference datasets for language model training. Unlike traditional Direct Preference Optimization (DPO) which relies on human annotations or simple heuristics, TPO extracts preference signals directly from the structural properties of conversation graphs, incorporating hindsight knowledge and topological awareness to create more accurate and cont

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We introduce **Topological Preference Optimization (TPO)**, a novel training methodology that leverages conversation topology and spatial-temporal coordinates to generate preference datasets for language model training. Unlike traditional Direct Preference Optimization (DPO) which relies on human annotations or simple heuristics, TPO extracts preference signals directly from the structural properties of conversation graphs, incorporating hindsight knowledge and topological awareness to create more accurate and contextually informed training data.

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