FAN simplifies expressive flow policies and distributional critics in offline RL via single-iteration behavior regularization and single-sample noise conditioning to claim SOTA performance with lower training and inference time.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A monograph organizing deep learning theory literature into a unified narrative from classical foundations to modern phenomena like scaling laws and emergence.
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Towards Efficient and Expressive Offline RL via Flow-Anchored Noise-conditioned Q-Learning
FAN simplifies expressive flow policies and distributional critics in offline RL via single-iteration behavior regularization and single-sample noise conditioning to claim SOTA performance with lower training and inference time.
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From Approximation to Emergence: A Theory of Deep Learning
A monograph organizing deep learning theory literature into a unified narrative from classical foundations to modern phenomena like scaling laws and emergence.