Title: Decoupled Q-Chunking
Primary area: reinforcement learning
Keywords: Reinforcement learning, action chunking, offline RL

Abstract: Bootstrapping bias problem is a long-standing challenge in temporal-difference (TD) methods in off-policy reinforcement learning (RL). Multi-step return backups can alleviate this issue but require delicate importance sampling to correct their off-policy bias. Recent work has proposed to use chunked critics, which estimate the value of short action sequences ("chunks") rather than individual actions, enabling unbiased multi-step backup. However, extracting policies from chunked critics is challenging: policies must output the entire action chunk open-loop, which can be sub-optimal for environments that require policy reactivity and also challenging to model especially when the chunk length grows. Our key insight is to decouple the chunk length of the critic from that of the policy, allowing the policy to operate over shorter action chunks. We propose a novel algorithm that achieves this by optimizing the policy against a distilled critic for partial action chunks, constructed by optimistically backing up from the original chunked critic to approximate the maximum value achievable when a partial action chunk is extended to a complete one. This design retains the benefits of multi-step value propagation while sidestepping both the open-loop sub-optimality and the difficulty of learning action chunking policies for long action chunks. We evaluate our method on challenging, long-horizon offline goal-conditioned benchmarks and shows that it reliably outperforms prior methods.