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RynnValue model scales robotic learning using temporal distance · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced RynnValue, an open-source value foundation model for robotic manipulation that utilizes temporal distance as a supervision target. This approach allows the model to scale to over 7,000 hours of data without requiring preference or progress annotations. RynnValue achieved a Kendall's tau_a of 0.675 on the RBM-EVAL-OOD benchmark, outperforming existing state-of-the-art methods. When converted into dense rewards, RynnValue significantly improved real-world policy success rates, demonstrating the effectiveness of temporal distance for generalist robot policies. AI

IMPACT Establishes temporal distance as a scalable supervision target for generalist robot policies, potentially accelerating progress in robot learning.

RANK_REASON The cluster describes a new research paper detailing a novel model and methodology for robotic learning.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

RynnValue model scales robotic learning using temporal distance · 2 sources tracked

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The cluster describes a new research paper detailing a novel model and methodology for robotic learning.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · 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 ·

    RynnValue: Scaling Robotic Value Foundation Models with Temporal Distance

    arXiv:2608.09853v1 Announce Type: cross Abstract: 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…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    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 pref…

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