arXiv:2610.41949v1 Announce Type: cross
Abstract: Few-step flow models generate samples through a handful of large denoising transitions. Applying GRPO to such models requires introducing stochastici...
By Zhuguanyu Wu, Congjia Chen, Ruihao Gong, Yang Yong, Shiqiao Gu, Yi Xu, Xianglong Liu
The paper introduces a sample‑efficient asynchronous Q‑learning algorithm for robust reinforcement learning in infinite‑horizon, discounted robust MDPs with total‑variation uncertainty. It proves that, given a single Markovian trajectory, the algorithm achieves an entrywise ε‑approximation of the optimal Q‑function with a sample complexity bounded by a nearly minimax‑optimal expression involving the discount factor, uncertainty level, mixing time, and occupancy probability. This result improves significantly over prior bounds for robust RL.
By Yuchen Jiao, Gen Li
arXiv:2602. 00781v2 Announce Type: replace Abstract: Online reinforcement learning in non-episodic, finite-horizon MDPs remains underexplored and is challenged by the need to estimate returns to a fixed terminal time.
By Jiamin Xu, Kyra Gan
arXiv:2610.07899v1 Announce Type: cross
Abstract: Generative actors are transforming offline reinforcement learning (RL) by enabling expressive policy classes that model complex action distributions....
By Guhyeon Kang, Minhae Kwon
The monograph "Statistical Foundations of Reinforcement Learning: A Non-Asymptotic Perspective" surveys recent advances in finite-sample statistical theory for reinforcement learning. It focuses on tabular Markov decision processes and examines three sampling settings—generative model, online, and offline RL—alongside model-based and model-free algorithmic paradigms. The work emphasizes minimax-optimal sample complexity, provides self-contained analyses, and briefly explores extensions such as robust, multi-agent, federated, and human-feedback RL.
By Yuxin Chen, Yuejie Chi, Jianqing Fan, Gen Li, Yuting Wei, Yuling Yan
arXiv:2602. 05459v2 Announce Type: replace Abstract: Offline goal-conditioned reinforcement learning (GCRL) is typically benchmarked by the best tuned success rate of each method.
By Jan Malte T\"opperwien, Aditya Mohan, Marius Lindauer