机器人学奖励，不再需要人打分
训练机器人干活，过去得靠人给每个动作打分或标进度，又慢又贵。这篇换了个思路：直接用时间差当奖励——离目标越近，时间差越小，奖励越高。时间戳是现成的，不用人标，所以能一口气喂进7000小时、300万条视频。结果在没见过的任务、机器人、视角上，判断好坏的能力超过了靠人工打分的老方法；把奖励喂回真机器人，成功率从52.5%提到72.5%。它不是你明天就能用的，但给机器人学技能指了条不用人盯着的路。
📄 原文摘要(英文)
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.