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[2510.22512] Transitive RL: Value Learning via Divide and Conquer
Summary
The paper introduces Transitive Reinforcement Learning (TRL), a novel value learning algorithm that enhances offline goal-conditioned reinforcement learning by using a divide-and-conquer approach, improving efficiency and performance in complex tasks.
Why It Matters
TRL addresses key challenges in offline goal-conditioned reinforcement learning, such as bias accumulation and high variance, making it a significant advancement in the field. Its divide-and-conquer methodology could lead to more effective learning strategies in various AI applications, particularly in long-horizon tasks.
Key Takeaways
- TRL offers a new algorithmic approach to value learning in reinforcement learning.
- It reduces bias accumulation compared to traditional temporal difference methods.
- The divide-and-conquer strategy enhances performance in long-horizon tasks.
- TRL outperforms existing offline goal-conditioned reinforcement learning algorithms.
- Dynamic programming in TRL minimizes variance issues common in Monte Carlo methods.
Computer Science > Machine Learning arXiv:2510.22512 (cs) [Submitted on 26 Oct 2025 (v1), last revised 23 Feb 2026 (this version, v2)] Title:Transitive RL: Value Learning via Divide and Conquer Authors:Seohong Park, Aditya Oberai, Pranav Atreya, Sergey Levine View a PDF of the paper titled Transitive RL: Value Learning via Divide and Conquer, by Seohong Park and 3 other authors View PDF Abstract:In this work, we present Transitive Reinforcement Learning (TRL), a new value learning algorithm based on a divide-and-conquer paradigm. TRL is designed for offline goal-conditioned reinforcement learning (GCRL) problems, where the aim is to find a policy that can reach any state from any other state in the smallest number of steps. TRL converts a triangle inequality structure present in GCRL into a practical divide-and-conquer value update rule. This has several advantages compared to alternative value learning paradigms. Compared to temporal difference (TD) methods, TRL suffers less from bias accumulation, as in principle it only requires $O(\log T)$ recursions (as opposed to $O(T)$ in TD learning) to handle a length-$T$ trajectory. Unlike Monte Carlo methods, TRL suffers less from high variance as it performs dynamic programming. Experimentally, we show that TRL achieves the best performance in highly challenging, long-horizon benchmark tasks compared to previous offline GCRL algorithms. Comments: Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv...
