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[2511.07730] Multistep Quasimetric Learning for Scalable Goal-conditioned Reinforcement Learning
Summary
This paper presents a novel approach to goal-conditioned reinforcement learning (GCRL) using multistep quasimetric learning, demonstrating improved performance in long-horizon tasks and real-world robotic manipulation.
Why It Matters
The research addresses a critical challenge in AI regarding long-horizon goal-reaching tasks, offering a method that integrates local and global updates for better performance. This advancement is significant for applications in robotics and AI, where efficient learning from visual observations is essential.
Key Takeaways
- Introduces a multistep quasimetric learning method for GCRL.
- Outperforms existing offline GCRL methods in long-horizon tasks.
- Enables effective stitching in real-world robotic manipulation.
- Demonstrates robust horizon generalization from offline datasets.
- Integrates local and global update strategies for improved learning.
Computer Science > Machine Learning arXiv:2511.07730 (cs) [Submitted on 11 Nov 2025 (v1), last revised 22 Feb 2026 (this version, v3)] Title:Multistep Quasimetric Learning for Scalable Goal-conditioned Reinforcement Learning Authors:Bill Chunyuan Zheng, Vivek Myers, Benjamin Eysenbach, Sergey Levine View a PDF of the paper titled Multistep Quasimetric Learning for Scalable Goal-conditioned Reinforcement Learning, by Bill Chunyuan Zheng and 3 other authors View PDF Abstract:Learning how to reach goals in an environment is a longstanding challenge in AI, yet reasoning over long horizons remains a challenge for modern methods. The key question is how to estimate the temporal distance between pairs of observations. While temporal difference methods leverage local updates to provide optimality guarantees, they often perform worse than Monte Carlo methods that perform global updates (e.g., with multi-step returns), which lack such guarantees. We show how these approaches can be integrated into a practical offline GCRL method that fits a quasimetric distance using a multistep Monte-Carlo return. We show our method outperforms existing offline GCRL methods on long-horizon simulated tasks with up to 4000 steps, even with visual observations. We also demonstrate that our method can enable stitching in the real-world robotic manipulation domain (Bridge setup). Our approach is the first end-to-end offline GCRL method that enables multistep stitching in this real-world manipulation dom...