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It is found that though continual learning methods help, fine-tuning reduces robustness across pretrained models, suggesting that starting with the strongest foundation model is not necessarily the best approach for performance on specialist tasks.
Across manipulation and reasoning tasks, PDE enables RL to learn successful policies even from zero-reward starts, and improves sample efficiency more broadly.
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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