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Offline RL with Hierarchical Action Chunking

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Computer Science > Machine Learning

arXiv:2607.20834 (cs)
[Submitted on 23 Jul 2026]

Title:Offline RL with Hierarchical Action Chunking

Authors:Ahad Jawaid
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Abstract:Offline goal-conditioned reinforcement learning (RL) holds the promise of learning general-purpose policies from static datasets. However, scaling these methods to long-horizon tasks remains a challenge due to the curse of horizon, where value estimation errors can compound through long chains of bootstrapped Bellman backups. Existing hierarchical approaches mitigate this by decomposing tasks into subgoals, yet they often rely on low-level controllers that suffer from myopic execution and biased value estimates. In this work, we propose Hierarchical Implicit Q-Chunking (HiQC), an offline goal-conditioned RL algorithm that combines high-level latent planning with low-level action chunking. By conditioning the low-level critic on temporally extended action sequences, HiQC enables unbiased k-step value backups, compressing the horizon at both the planning and execution levels. We theoretically demonstrate that this dual decomposition results in a tighter bound on value error under a bounded per-backup error model compared to standard hierarchy or flat chunking alone. Empirically, HiQC achieves the highest aggregate performance among the compared methods on the OGBench suite, with its largest gains on long-horizon navigation tasks such as humanoid-giant.
Comments: RLC/RLJ 2026
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2607.20834 [cs.LG]
  (or arXiv:2607.20834v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2607.20834
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Ahad Jawaid [view email]
[v1] Thu, 23 Jul 2026 01:48:46 UTC (712 KB)
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