Towards Efficient and Expressive Offline RL via Flow-Anchored Noise-conditioned Q-Learning

arXiv:2605.01663v2 Announce Type: replace Abstract: We propose Flow-Anchored Noise-conditioned Q-Learning (FAN), a highly efficient and high-performing offline reinforcement learning (RL) algorithm. Recent work has shown that expressive flow policies and distributional critics improve offline RL performance, but at a high computational cost. Specifically, flow policies require iterative sampling to produce a single action, and distributional critics require computation over multiple samples (e.g., quantiles) to estimate value. To address these inefficiencies while maintaining high performance,

Source: arXiv cs.LG — read the full report at the original publisher.

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