RynnValue：用时间距离扩展机器人价值基础模型
RynnValue: Scaling Robotic Value Foundation Models with Temporal Distance
想搞机器人奖励模型但被标注烦死的看过来，RynnValue直接用时间戳当监督，不用偏好标注，效果还反超SOTA，成功率涨了20个点。
RynnValue是一个开源的机器人操作价值基础模型，用时间距离替代偏好或进度作为监督信号，可从时间戳直接生成标签，无需额外标注。
该模型在7000多小时、约300万条指令条件片段上训练，在RBM-EVAL-OOD基准上平均Kendall's tau_a达0.675，超过偏好监督的SOTA（0.
655），并是仅进度监督基线（0.292）的两倍多。
通过势能塑形转为稠密奖励后，RynnValue将真实世界策略成功率从52.5%提升至72.
5%（在线），离线从63.8%提升至82.
5%。
RynnValue: Scaling Robotic Value Foundation Models with Temporal Distance
General-purpose reward models are increasingly the bottleneck for scaling robot learning, yet the recipe for learning value-related capabilities from large-scale heterogeneous corpora remains underexplored. Existing approaches tie supervision to task-internal anchors such as preferences or normalized progress, none of which transfer cleanly across embodiments and data sources. We introduce RynnValue, an open-source value foundation model for robotic manipulation that replaces these anchors with temporal distance, the directed cost-to-go from an observation to the language-specified goal. Because temporal-distance labels can be derived directly from timestamps, RynnValue scales to over 7,000 hours and roughly 3M instruction-conditioned clips without preference or progress annotations. To make temporal-value learning reliable at scale, we combine random temporal sampling, temporal-order shuffling, and value-isolation attention, suppressing shortcuts that would leave predictions insensitive to failures and regressions. Trained without preference labels, RynnValue attains an average Kendall's tau_a of 0.675 on RBM-EVAL-OOD, surpassing the fully preference-supervised state of the art (0.655) and more than doubling a progress-only counterpart (0.292), while generalizing zero-shot to unseen tasks, embodiments, and viewpoints. Converted into dense rewards via potential-based shaping, it raises real-world policy success from 52.5% to 72.5% online and from 63.8% to 82.5% offline. These results establish temporal distance as a scalable supervision target and practical reward interface for generalist robot policies.
This report is categorized under AITOP's "Papers" desk. Key entities and technical topics under continuous tracking include RynnValue, 机器人操作, 价值模型, 时间距离.
Source attribution: curated from arXiv cs.LG (@Dongchi Huang, Hongyin Zhang, Bohan Hou, Siteng Huang, Zhian Su, Hang Guo, Tong Lu, Zhaofeng Xu, Jiahao Tang, Jianfei Yang, Donglin Wang, Peixi Peng, Mingxiu Chen, Deli Zhao, Xin Li).
