Automating Staged Rollout with Reinforcement Learning

2022-04-01 · Shadow Pritchard, Vidhyashree Nagaraju, Lance Fiondella

Staged rollout is a strategy of incrementally releasing software updates to portions of the user population in order to accelerate defect discovery without incurring catastrophic outcomes such as system wide outages. Some past studies have examined how to quantify and automate staged rollout, but stop short of simultaneously considering multiple product or process metrics explicitly. This paper demonstrates the potential to automate staged rollout with multi-objective reinforcement learning in order to dynamically balance stakeholder needs such as time to deliver new features and downtime incurred by failures due to latent defects.

📄 PDF Abstract BibTeX arXiv:2204.02189

Code (0)

등록된 구현이 없습니다.

Tasks

Multi-Objective Reinforcement Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

FlowForge: A Staged Local Rollout Engine for Flow-Field Prediction

2026-04-21 · Xiaowen Zhang, Ziming Zhou, Fengnian Zhao, David L. S. Hung arxiv

Deep learning surrogates for CFD flow-field prediction often rely on large, complex models, which can be slow and fragile when data are noisy or incomplete. We introduce FlowForge, a staged local rollout engine that pred…

Tree-OPO: Off-policy Monte Carlo Tree-Guided Advantage Optimization for Multistep Reasoning

2025-09-11 · Bingning Huang, Tu Nguyen, Matthieu Zimmer arxiv

Recent advances in reasoning with large language models (LLMs) have shown the effectiveness of Monte Carlo Tree Search (MCTS) for generating high quality intermediate trajectories, particularly in math and symbolic domai…

Reinforcement Learning Mathematical Reasoning

Efficient Multi-turn RL for GUI Agents via Decoupled Training and Adaptive Data Curation

2025-09-28 · Pengxiang Li, Zechen Hu, Zirui Shang, Jingrong Wu 외 arxiv

Vision-language model (VLM) based GUI agents show promise for automating complex desktop and mobile tasks, but face significant challenges in applying reinforcement learning (RL): (1) slow multi-turn interactions with GU…

Reinforcement Learning

UI-S1: Advancing GUI Automation via Semi-online Reinforcement Learning

2025-09-15 · Zhengxi Lu, Jiabo Ye, Fei Tang, Yongliang Shen 외 arxiv

Graphical User Interface (GUI) agents have demonstrated remarkable progress in automating complex user interface interactions through reinforcement learning. However, current approaches face a fundamental dilemma: offlin…

Reinforcement Learning Offline RL

TeleBoost: A Systematic Alignment Framework for High-Fidelity, Controllable, and Robust Video Generation

2026-02-07 · Yuanzhi Liang, Xuan'er Wu, Yirui Liu, Yijie Fang 외 arxiv

Post-training is the decisive step for converting a pretrained video generator into a production-oriented model that is instruction-following, controllable, and robust over long temporal horizons. This report presents a …

Reinforcement Learning Video Generation