arxiv
PublishedSeptember 30, 2026 at 4:00 AM
ChronoSRL: Temporal Geometry for Self-Supervised Reinforcement Learning
Publisher summary· verbatim
arXiv:2609.36238v1 Announce Type: new Abstract: A goal that is close in space can be far away in time. Obstacles, terrain, and the agent's own capabilities determine how long it takes to get there. Yet, critics in contrastive and survival reinforcement learning do not measure the distances in their
Stay posted· Newsletter
A 5-min weekly brief — top movers, price watch, story of the week.
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivRight Words, Wrong Moment: A Clinician-Grounded Analysis of Distress in 19,930 Conversations between Young People and ChatGPT59marxivSAGE: A Statistical Acceptance Gate for Self-Evolving Agents59marxivPowerZooJax: A JAX-based Power System Benchmark for Reinforcement Learning59marxivGeoWind2Plan: Mission-Time 3D Urban Wind Prediction for Energy-Efficient UAV Planning59mThe Bubble Brief
WEEKLYRead AI insights every Tuesday — top movers, new releases, story of the week.
Originally published on arxiv ↗