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arXiv · 2508.14027

Learning from Preferences and Mixed Demonstrations in General Settings

Jason R Brown·Carl Henrik Ek·Robert D Mullins
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Abstract

Reinforcement learning is a general method for learning in sequential settings, but it can often be difficult to specify a good reward function when the task is complex. In these cases, preference feedback or expert demonstrations can be used instead. However, existing approaches utilising both together are often ad-hoc, rely on domain-specific properties, or won't scale. We develop a new framing for learning from human data, \emph{reward-rational partial orderings over observations}, designed to be flexible and scalable. Based on this we introduce a practical algorithm, LEOPARD: Learning Estimated Objectives from Preferences And Ranked Demonstrations. LEOPARD can learn from a broad range of data, including negative demonstrations, to efficiently learn reward functions across a wide range of domains. We find that when a limited amount of preference and demonstration feedback is available, LEOPARD outperforms existing baselines by a significant margin. Furthermore, we use LEOPARD to investigate learning from many types of feedback compared to just a single one, and find that combining feedback types is often beneficial.

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Jason R Brown, Carl Henrik Ek, Robert D Mullins. 2025-08-19. Learning from Preferences and Mixed Demonstrations in General Settings. https://arxiv.org/abs/2508.14027

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