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Work2Skill 提出一个框架，通过 WorkRecorder 收集人类 GUI 操作演示，构建 WorkDemo-350 多模态数据集，并用智能体式技能归纳方法从人类经验中提炼可复用的智能体技能，在 MyPCBench 和 OSWorld-Verified 上显著提升了多种执行模型的任务表现。
该论文提出WATCH，一种跨视频预训练方法，通过任务配对的人类演示学习可迁移的世界-动作模型，无需人-机器人配对数据；在机器人后训练后将MimicDroid的上下文内成功率从40.9%提升至68.3%（Cosmos Policy）以及从59.8%提升至77.7%（Cosmos 3）。
This paper from MIT proposes an adversarial generator-discriminator framework that combines verifiable rewards with a learned signal from human demonstrations to address issues like diversity collapse, unnatural responses, and reward hacking in RLVR training of language models.
A research paper that combines a small amount of human demonstrations as a regularization objective with self-play reinforcement learning, enabling human-compatible driving policies using far less human data (30 minutes vs thousands of hours) and training in 15 hours on a single consumer GPU.
OpenAI co-organizes the MineRL 2020 Competition to advance sample-efficient reinforcement learning algorithms that leverage human demonstrations. Participants compete to obtain a diamond in Minecraft using only 8 million simulator samples and 4 days of single-GPU training, with access to a 60+ million frame human demonstration dataset.