aiqbench analysis
This research introduces PATH, a framework that explicitly models prerequisite relations in reinforcement learning environments to optimize training paths via active learning. By leveraging the structure of curriculum graphs, the method improves robustness and generalization across diverse domains without relying on implicit heuristics. The work represents a significant methodological advance for scaling RL agents through structured exploration rather than brute-force sampling.
Active Curriculum Refinement for Reinforcement Learning
arXiv:2608.26469v1 Announce Type: new Abstract: In many reinforcement learning (RL) domains, environments are connected by prerequisite relations, such as difficulty-increasing edits or parameter increments, which induce a directed acyclic curriculum graph (DAG). Although this structure is often exploited only implicitly, explicitly modeling it can improve training. We introduce PATH, a curriculum-learning framework that performs active learning over the curriculum graph. PATH first expands coverage by sampling diverse curriculum paths and then reallocates training toward regions that remain unmastered. Experiments across diverse environments show that PATH explicitly leverages the graph structure to achieve strong robustness and generalization.
Abstract:In many reinforcement learning (RL) domains, environments are connected by prerequisite relations, such as difficulty-increasing edits or parameter increments, which induce a directed acyclic curriculum graph (DAG). Although this structure is often exploited only implicitly, explicitly modeling it can improve training. We introduce PATH, a curriculum-learning framework that performs active learning over the curriculum graph. PATH first expands coverage by sampling diverse curriculum paths and then reallocates training toward regions that remain unmastered. Experiments across diverse environments show that PATH explicitly leverages the graph structure to achieve strong robustness and generalization.
Machine Learning (cs.LG)
arXiv:2608.26469 [cs.LG]
(or arXiv:2608.26469v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.26469 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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