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Reward-free Pretraining for Reinforcement Learning via Occupancy Coverage Maximization
ROVER pretrains exploration policies via occupancy coverage maximization using a resolvent world model and virtual sink state, yielding stronger initializations for sparse-reward downstream tasks.
Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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AI panel: 9 of 20 reviewers recommend it
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medium 5/10
strict 0/5