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Computer Science > Machine Learning
[Submitted on 8 May 2026] Title:Theoretical Limits of Language Model Alignment View a PDF of the paper titled Theoretical Limits of Language Model Alignment, by Lucas Monteiro Paes and Natalie Mackraz and Barry-John Theobald and Federico Danieli View PDF HTML (experimental) Abstract:Language model (LM) alignment improves model outputs to reflect human preferences while preserving the capabilities of the base model.
PREDICT: Preference Reasoning by Evaluating Decomposed preferences Inferred from Candidate Trajectories
[Submitted on 8 Oct 2024] Title:PREDICT: Preference Reasoning by Evaluating Decomposed preferences Inferred from Candidate Trajectories View a PDF of the paper titled PREDICT: Preference Reasoning by Evaluating Decomposed preferences Inferred from Candidate Trajectories, by Stephane Aroca-Ouellette and 3 other authors View PDF HTML (experimental) Abstract:Accommodating human preferences is essential for creating AI agents that deliver personalized and effective interactions.
[2211.06527] Rewards Encoding Environment Dynamics Improves Preference-based Reinforcement Learning
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