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Beyond One-Size-Fits-All: Diagnosis-Driven Online Reinforcement Learning with Offline Priors

NeurIPS 2026 · Reinforcement learning › Offline RL · approach: Simple practical offline RL · 0 citations (OpenAlex)

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Position paper arguing online RL with offline priors should shift from one-size-fits-all benchmarks to diagnosis-driven tension management, supported by controlled offline-to-online RL experiments on D4RL showing reliance reversals across tasks.

Problem

Offline priors (pre-trained policies, offline data, world models, reward models) help online RL but introduce a tension between reliance and adaptation whose optimal setting varies across deployments, which benchmark-driven evaluation fails to capture.

Approach

Frames offline priors as "bounded commitments" with uncertain validity, formalizes three reliance parameters (initialization mu, reference lambda, auxiliary beta), and advocates diagnosis-driven tension management that uses online evidence to assess prior-deployment match and monitor tension dynamics.

Key results

• Offline-trained initialization (RLPD protocol) helps on some D4RL tasks but hurts on others, showing the optimal initialization reliance is task-dependent.
• Cal-QL conservative penalty versus unconstrained SAC reverses sign across tasks, both with and without offline data retention.
• Retaining offline data in the replay buffer helps on some tasks and hurts on others; no setting of mu, lambda, or beta is uniformly beneficial.

Surprising findings

• Toggling any single reliance parameter yields help-or-hurt reversals across tasks, and the effect of one parameter depends on the others.

Limitations

• Empirical evidence is limited to offline-to-online RL on D4RL; cross-domain claims rely on cited work rather than new experiments.
• No concrete diagnostic method is implemented or evaluated.
• Reliance parameters are abstracted as continuous quantities but tested as binary toggles.

Experimental setup

Datasets: D4RL

Benchmarks: D4RL

Metrics: episodic return

Baselines: RLPD Cal-QL SAC

Builds on: RLPD Cal-QL SAC

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