robotics
The World Model Searched Without Training on Imagined Data
QWM used predicted trajectories to select actions while keeping policy and value learning grounded in real transitions.
Summary
QWM used predicted trajectories to select actions while keeping policy and value learning grounded in real transitions.
Test-time search over imagined rollouts improved sample efficiency and performance on Robomimic and LIBERO in the authors’ comparison. Avoiding direct training on imagined transitions reduces one source of compounding bias but does not eliminate world-model error during action selection.
Why it matters
QWM used predicted trajectories to select actions while keeping policy and value learning grounded in real transitions.
Limits and context
- Avoiding direct training on imagined transitions reduces one source of compounding bias but does not eliminate world-model error during action selection.
Key claims
QWM used predicted trajectories to select actions while keeping policy and value learning grounded in real transitions.
Qualification: Avoiding direct training on imagined transitions reduces one source of compounding bias but does not eliminate world-model error during action selection.
Evidence: source-2026-08-19-019
Sources
- arXiv preprint 2608.17163arXiv · primary research
Corrections
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