RynnValue: Scaling Robotic Value Foundation Models with Temporal Distance

arXiv 2026 16.5 method, system

TLDR

RynnValue is a robotic value foundation model using temporal distance as supervision, scaling to 7,000 hours and improving real-world policy success.

Reasoning

The paper introduces a novel value learning approach with temporal distance, showing strong empirical results on benchmarks and real-world tasks. However, it does not address world models or simulation, and the abstract lacks details on limitations.

Read-first score

Read-first score 16.5, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 0.

Recency 6%

100

Uses a gentle age decay so recent papers surface without erasing older foundations. 2026

Methodology quality 18%

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=result

Reproducibility 18%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=none

Topical relevance 29%

Uses existing LLM keyword relevance scores normalized to 0-100. world model,world simulator,generative world model,interactive world model,video world model,world dynamics prediction,model-based reinforcement learning world model

Citation impact 18%

Uses OpenAlex-shaped citation metadata as a bibliometric attention signal, separate from paper quality. cited_by_count=0

Citation velocity 12%

Citation velocity estimates citations per publication-year to reduce old-paper bias. velocity=0.00

Field roles

Frontier

Rank sensitivity

Stability: volatile; rank range: 20.

Keyword Scores

world model

world simulator

generative world model

interactive world model

video world model

world dynamics prediction

model-based reinforcement learning world model

Resources

Paper