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arXiv · 2603.13971

Chunk-Guided Q-Learning

Gwanwoo Song·Kwanyoung Park·Youngwoon Lee
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Abstract

In offline reinforcement learning (RL), single-step temporal-difference (TD) learning can suffer from bootstrapping error accumulation over long horizons. Action-chunked TD methods mitigate this by backing up over multiple steps, but can introduce suboptimality by restricting the policy class to open-loop action sequences. To resolve this trade-off, we present Chunk-Guided Q-Learning (CGQ), a single-step TD algorithm that guides a fine-grained single-step critic by regularizing it toward a chunk-based critic trained using temporally extended backups. This reduces compounding error while preserving fine-grained value propagation. We theoretically show that CGQ attains tighter critic optimality bounds than either single-step or action-chunked TD learning alone. Empirically, CGQ achieves strong performance on challenging long-horizon OGBench tasks, often outperforming both single-step and action-chunked methods.

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Gwanwoo Song, Kwanyoung Park, Youngwoon Lee. 2026-03-14. Chunk-Guided Q-Learning. https://arxiv.org/abs/2603.13971

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