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.