Designing Safe, Sample-Efficient, and Robust Reinforcement Learning for Ranking and Diffusion Models

This paper proposes a reinforcement‑learning framework that simultaneously ensures safety, sample efficiency, and robustness, applying a contextual‑bandit perspective to ranking/recommendation systems and text‑to‑image diffusion models, and introduces novel algorithms for safe deployment, variance‑reduced off‑policy estimation, and a LOOP method for generative RL.

RobustnessSafetycontextual bandits

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Designing Safe, Sample-Efficient, and Robust Reinforcement Learning for Ranking and Diffusion Models

DataFunTalk

Feb 21, 2021 · Artificial Intelligence

Advances in Pre‑Ranking for Large‑Scale Advertising: The COLD Framework and Its Technical Evolution

This article reviews the development history, technical routes, and recent breakthroughs of pre‑ranking (coarse ranking) in large‑scale advertising systems, focusing on Alibaba's COLD (Computing‑power‑cost‑aware Online and Lightweight Deep) framework, its model design, engineering optimizations, experimental results, and future research directions.

AdvertisingCOLDOnline Learning

0 likes · 20 min read

Advances in Pre‑Ranking for Large‑Scale Advertising: The COLD Framework and Its Technical Evolution

DataFunTalk

Sep 3, 2020 · Artificial Intelligence

Deep Learning Practices for Click‑Through‑Rate Prediction and Ranking at 58.com

This article describes how 58.com applied deep‑learning techniques—including feature engineering, sample construction, model evolution from Wide&Deep to DIN/DIEN and multi‑task learning—and system‑level optimizations to improve CTR/CPM performance in its large‑scale commercial ranking platform.

CTR predictionDeep LearningSystem optimization

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Deep Learning Practices for Click‑Through‑Rate Prediction and Ranking at 58.com