Papers by Imranul Ashrafi

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Challenge:	Recent work suggests that test-time alignment methods are inefficient because they require a large number of computational resources.
Approach:	They propose a preference-based training framework that uses a multi-objective value function to better reflect the structure of preference data.
Outcome:	The proposed framework outperforms RE-Control and shows greater generalization on out-of-domain datasets.