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Across manipulation and reasoning tasks, PDE enables RL to learn successful policies even from zero-reward starts, and improves sample efficiency more broadly.
This paper proposes an alternative approach in which computationally intensive VLM queries are made during train-time to learn a lightweight latent memory that can be efficiently queried at deployment time, and demonstrates that the workspace tokens are not only more lightweight, but also lead to better policy performa...
Learning from Hindsight is presented, which brings hindsight relabeling to RL post-training of VLAs by scoring failed rollouts against the tasks they actually achieved, and achieves 5 times improvement in sample efficiency, and outperforms a dense progress-reward baseline on out-of-distribution LIBERO-PRO tasks.
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