Research·arXiv cs.LG·14h ago

Offline RL framework improves policy learning from static datasets via adaptive queries

Researchers propose a framework addressing a core bottleneck in offline reinforcement learning: how to extract maximum value from fixed datasets without environment interaction. The work tackles query selection and feedback integration by moving beyond simple distance metrics to uncertainty-aware preference elicitation, paired with adaptive regularization. This matters because offline RL underpins real-world deployment scenarios where live interaction is costly or risky, from robotics to autonomous systems. Better methods here directly improve policy learning efficiency in production-constrained settings where data reuse is non-negotiable.

Modelwire context

Explainer

The paper's core novelty isn't just adding uncertainty to offline RL, but specifically using it to guide which preference queries to ask. Most prior work treats query selection as a separate problem from feedback integration; this work couples them through uncertainty estimation, reducing the number of human annotations needed to reach the same policy quality.

This connects directly to the S3 paper from the same day, which also tackles uncertainty-aware decision-making in constrained learning settings. Where S3 uses coarse dynamics models to guide hierarchical agents toward better subgoals, this work uses uncertainty estimates to guide which human feedback to request in offline settings. Both papers share a common insight: uncertainty quantification can replace exhaustive exploration or annotation, improving sample efficiency when data collection is expensive. The difference is scope: S3 operates within a single agent's planning, while this framework operates across the human-in-the-loop annotation boundary.

If this method is benchmarked on standard offline RL datasets (D4RL) with human preference labels already collected, check whether the uncertainty-guided query selection achieves 80%+ of full-annotation performance using fewer than 30% of preference pairs. If that threshold holds, it signals practical deployment viability; if not, the method may be solving a narrower problem than offline RL practitioners face.

Coverage we drew on

S3: Stable Subgoal Selection by Constraining Uncertainty of Coarse Dynamics in Hierarchical Reinforcement Learning · arXiv cs.LG

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MentionsOffline reinforcement learning · Preference queries · Value regularization · Policy optimization

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Modelwire summarizes, we don’t republish. arXiv cs.LG originally reported this story as “Conservative Query and Adaptive Regularization for Offline RL Under Uncertainty Estimation”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.