A multi-task multi-modal transformer policy pretrained on offline trajectories from thousands of RL environments matches task-specific reference policies on approximately 1000 environments spanning robotics, driving, inventory, cybersecurity, trading, and games.
Gallouédec, E
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
PDF improves VLA success rates on LIBERO and Atari by applying test-time perturbation learning with delayed feedback to correct trajectory overfitting and overconfidence.
WestWorld introduces a scalable trajectory world model with Sys-MoE routing via system embeddings and structural embeddings for physical knowledge, pretrained on 89 environments to improve zero-shot prediction and real-robot control.
citing papers explorer
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Towards Scalable Multi-Task Reinforcement Learning with Large Decision Models
A multi-task multi-modal transformer policy pretrained on offline trajectories from thousands of RL environments matches task-specific reference policies on approximately 1000 environments spanning robotics, driving, inventory, cybersecurity, trading, and games.
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Test-Time Perturbation Learning with Delayed Feedback for Vision-Language-Action Models
PDF improves VLA success rates on LIBERO and Atari by applying test-time perturbation learning with delayed feedback to correct trajectory overfitting and overconfidence.
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WestWorld: A Knowledge-Encoded Scalable Trajectory World Model for Diverse Robotic Systems
WestWorld introduces a scalable trajectory world model with Sys-MoE routing via system embeddings and structural embeddings for physical knowledge, pretrained on 89 environments to improve zero-shot prediction and real-robot control.