From Reward-Free Representations to Preferences: Rethinking Offline Preference-Based Reinforcement Learning
arXiv:2606.01123v1 Announce Type: new Abstract: Preference-based reinforcement learning (PbRL) avoids explicit reward engineering by learning from pairwise human preference feedback. Existing offline PbRL methods typically follow a two-stage pipeline, first learning a reward or preference model from labeled preferences and then performing offline RL on unlabeled data. We revisit offline PbRL through the lens of reward-free representation learning (RFRL) from the zero-shot RL literature, and propose a new training framework that first learns latent successor-measure representations from reward-
The paper addresses current challenges in offline preference-based reinforcement learning by leveraging advancements in reward-free representation learning, indicating a natural evolution in AI research towards more efficient and robust learning paradigms.
This research could significantly improve the efficiency and applicability of AI systems that learn from human preferences without explicit reward engineering, broadening the scope for deploying autonomous agents in complex, real-world scenarios.
The proposed framework shifts from a two-stage reward modeling approach to a more integrated representation learning technique, potentially leading to more robust and less data-intensive preference-based RL systems.
- · AI researchers
- · Robotics companies
- · Autonomous system developers
- · AI ethics and alignment researchers
- · Companies reliant on extensive human labeling for RL
- · Current two-stage offline PbRL methodologies
More efficient and scalable development of AI agents that align with human values.
Accelerated deployment of autonomous systems in sectors like logistics, personalized services, and advanced manufacturing.
Increased societal debate on the ethical implications and control of increasingly autonomous AI agents learned through implicit preferences.
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Read at arXiv cs.LG