aiqbench analysis
This paper gives theoretical grounding to a common practice: unsupervised skill pretraining followed by goal-reaching. It shows that three standard goal-conditioned RL formulations can require incompatible optimal policies, so the choice of pretraining objective should depend on which downstream goal criterion you care about. The results are formal bounds rather than empirical benchmarks, so practitioners should treat the suggested objective-matching as guidance to test, not a validated recipe.
Unifying Goal-Conditioned RL and Unsupervised Skill Learning via Control-Maximization
arXiv:2605.06145v2 Announce Type: replace Abstract: Unsupervised pretraining has driven empirical advances in goal-conditioned reinforcement learning (GCRL), but its theoretical foundations remain poorly understood. In particular, an influential class of methods, mutual information skill learning (MISL), discovers behaviorally diverse skills that can later be used for downstream goal-reaching. However, it remains a theoretical mystery why skills learned through MISL should support goal-reaching. A subtle challenge is that both GCRL and MISL are umbrella terms: Different GCRL tasks use distinct criteria for measuring goal-reaching performance, while different MISL methods optimize distinct notions of behavioral diversity. We address this challenge and unify GCRL and MISL as instances of control maximization. We identify three canonical GCRL formulations and prove that they are fundamentally inequivalent: They can induce incompatible optimal policies even in the same environment. Nevertheless, they all share a common interpretation: A well-performing goal-conditioned policy is one whose future trajectory is highly sensitive to the commanded goal, with the precise notion of sensitivity determined by the GCRL formulation. Noting that MISL objectives can be understood as measures of skill sensitivity akin to goal sensitivity, we show that MISL objectives are bounded by formulation-specific downstream goal sensitivities. These bounds establish a precise correspondence between MISL methods and downstream GCRL tasks: For every GCRL formulation, there exists a matching MISL objective for which more diverse skills afford greater downstream goal sensitivity. Our results thus lay a theoretical foundation for RL pretraining and have important practical implications, such as suggesting which pretraining objectives to use when a user cares about a specific class of downstream tasks.
Abstract:Unsupervised pretraining has driven empirical advances in goal-conditioned reinforcement learning (GCRL), but its theoretical foundations remain poorly understood. In particular, an influential class of methods, mutual information skill learning (MISL), discovers behaviorally diverse skills that can later be used for downstream goal-reaching. However, it remains a theoretical mystery why skills learned through MISL should support goal-reaching. A subtle challenge is that both GCRL and MISL are umbrella terms: Different GCRL tasks use distinct criteria for measuring goal-reaching performance, while different MISL methods optimize distinct notions of behavioral diversity. We address this challenge and unify GCRL and MISL as instances of control maximization. We identify three canonical GCRL formulations and prove that they are fundamentally inequivalent: They can induce incompatible optimal policies even in the same environment. Nevertheless, they all share a common interpretation: A well-performing goal-conditioned policy is one whose future trajectory is highly sensitive to the commanded goal, with the precise notion of sensitivity determined by the GCRL formulation. Noting that MISL objectives can be understood as measures of skill sensitivity akin to goal sensitivity, we show that MISL objectives are bounded by formulation-specific downstream goal sensitivities. These bounds establish a precise correspondence between MISL methods and downstream GCRL tasks: For every GCRL formulation, there exists a matching MISL objective for which more diverse skills afford greater downstream goal sensitivity. Our results thus lay a theoretical foundation for RL pretraining and have important practical implications, such as suggesting which pretraining objectives to use when a user cares about a specific class of downstream tasks.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
arXiv:2605.06145 [cs.LG]
(or arXiv:2605.06145v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.06145 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 354 words