New monograph by Yuxin Chen et al. on non-asymptomatic statistical foundations of RL
aminkarbasi · x · 2026-11-03
A new book, Statistical Foundations of Reinforcement Learning: A Non-Asymptotic Perspective (arXiv:2610.41903), by Yuxin Chen, Yuejie Chi, Jianqing Fan, Gen Li, Yuting Wei, and Yuling Yan, presents a self-contained treatment of modern non-asymptotic statistical RL theory.
- Focuses on tabular MDPs across three canonical settings: generative-model RL, online RL, and offline RL
- Covers both model-based and model-free paradigms
- Centers on sample complexity analysis and minimax-optimal algorithm design, drawing on high-dimensional statistics, information theory, and statistical learning theory
- Motivated by data-efficient RL in sample-starved, high-stakes applications
More from Research
- Apple's iPrefBench tests personal agents on 130 underspecified iOS tasks: infer or ask? — hhsun1 · 2026-11-03
- NeurIPS Paper Maps Where Temporal Context Belongs in Low-Resource Video Adaptation — HildeKuehne · 2026-11-03
- CRoS: rewarding causality on scrambled CoTs boosts faithfulness 13.8% on Gemma-3-4B — mohitban47 · 2026-11-03
- Google's 'sleep' paradigm lets LLMs consolidate memories and self-modify for continual learning — teortaxesTex · 2026-11-03
- Yale lab open-sources a Claude skill that makes publication-quality schematic figures — CatAstro_Piyush · 2026-11-03
- EMNLP 2026 interpretability tutorial slides released, praised by attendees — PMinervini · 2026-11-03