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News/ChronoSRL: Temporal Geometry for Self-Supervised Reinforcement Learning
arxiv
PublishedSeptember 30, 2026 at 4:00 AM

ChronoSRL: Temporal Geometry for Self-Supervised Reinforcement Learning

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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

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