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From Static Policies to Adaptive Priors in Offline Reinforcement Learning

NeurIPS 2026 · Reinforcement learning › Offline RL · approach: Offline RL sample complexity theory · 0 citations (OpenAlex)

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Position paper arguing offline RL should learn adaptive policy priors that preserve memory, exploration, and self-correction for test-time adaptation and online fine-tuning, with Bayesian offline RL as a principled direction.

Problem

Classical offline RL optimizes policies for direct deployment under conservatism, leaving little capacity for improvement when the offline-trained policy is later updated through test-time adaptation or online interaction.

Approach

Reframe the objective of the offline stage as learning adaptive policy priors (formalized as adaptive offline RL, AORL) that retain memory, exploration, and self-correction, and motivate this via a Bayesian epistemic POMDP view where the offline posterior over MDPs serves as the prior for online inference.

Key results

• No experiments are reported; the paper is a position paper with conceptual arguments and illustrative schematic figures only.

Limitations

• No empirical validation of the proposed AORL perspective or Bayesian direction.
• Exact Bayesian posterior inference over MDPs is intractable; scalable approximations remain open.
• Lacks established benchmarks for measuring test-time adaptability.

Experimental setup

Builds on: Bayes-adaptive MDPs offline-to-online RL conservative Q-learning Decision Transformer meta-RL

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