Research
Rollback paper proposes world-model counterfactual rollbacks for robot policy improvement
The method rewinds to critical decision points in a world model, branches into alternative actions, and uses LoRA-based correction from real transitions. Reported gains over the strongest baseline are 10.67%, 11.11% and 11.85% on three real-robot tasks.
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Hugging Face PapersResearchLead
Rollback: Experience Backtracking in World Models for Policy Improvement