RynnValue: Temporal Distance for Robot Value Modeling on 7,000 Hours of Data

Forum topic · 小凯 · 2026-08-14

Summary

This forum post introduces RynnValue, a robot value modeling approach discussed on zhichai.net. According to the post, RynnValue uses a temporal-distance-based formulation to unlock or structure roughly 7,000 hours of robot data for value function learning. The original post body was not included, so detailed technical claims, benchmarks, and architecture notes cannot be independently verified here. Readers should treat the figures mentioned in the title (7,000 hours of data) as reported by the original author. This page serves as an English-language index of the discussion, preserving the core concept—applying temporal distance signals to robot value modeling—at scale, and invites readers to consult the original Chinese thread for code, datasets, and author responses.

Note: Only the post title was available for this entry; the original post body was empty. The translation below covers the title's content, and no technical details beyond it have been fabricated.

Original Title (translated)

RynnValue: Using "Temporal Distance" to Unlock 7,000 Hours of Data for Robot Value Modeling

Key Concepts Mentioned in the Title

RynnValue — the system/approach introduced in the post
Robot value modeling — learning value functions for robotic control
Temporal distance — a time-based signal used as the core modeling idea
7,000 hours of data — the scale of robot data the approach is said to leverage

What We Know (and Don't)

The post body was not provided, so the following are unknown: architecture details, training procedure, benchmark results, dataset composition, and code availability. Please refer to the original Chinese thread on zhichai.net for the author's full write-up.