mlsanityData to 14 Oct 2026

Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale

arXiv 2311.08430 ↗First posted 14 Nov 2023cs.LG cs.AI cs.IRcited by 3

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Abstract (arXiv)

Neural Architecture Search (NAS) has demonstrated its efficacy in computer vision and potential for ranking systems. However, prior work focused on academic problems, which are evaluated at small scale under well-controlled fixed baselines. In industry system, such as ranking system in Meta, it is unclear whether NAS algorithms from the literature can outperform production baselines because of: (1) scale - Meta ranking systems serve billions of users, (2) strong baselines - the baselines are production models optimized by hundreds to thousands of world-class engineers for years since the rise of deep learning, (3) dynamic baselines - engineers may have established new and stronger baselines during NAS search, and (4) efficiency - the search pipeline must yield results quickly in alignment with the productionization life cycle. In this paper, we present Rankitect, a NAS software framework for ranking systems at Meta. Rankitect seeks to build brand new architectures by composing low level building blocks from scratch. Rankitect implements and improves state-of-the-art (SOTA) NAS methods for comprehensive and fair comparison under the same search space, including sampling-based NAS, one-shot NAS, and Differentiable NAS (DNAS). We evaluate Rankitect by comparing to multiple production ranking models at Meta. We find that Rankitect can discover new models from scratch achieving competitive tradeoff between Normalized Entropy loss and FLOPs. When utilizing search space designed by engineers, Rankitect can generate better models than engineers, achieving positive offline evaluation and online A/B test at Meta scale.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 29 of its 46 references that are in the map. First posted on arXiv 14 Nov 2023.

Builds on

29 of 46 references.

References · 29

  1. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
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17 more
  1. 2018SNAS: Stochastic Neural Architecture Searchcited by 289
  2. 2019Searching for A Robust Neural Architecture in Four GPU Hourscited by 260
  3. 2015Non-stochastic Best Arm Identification and Hyperparameter Optimizationcited by 221
  4. 2021Zero-Cost Proxies for Lightweight NAScited by 199
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  6. 2019BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Searchcited by 179
  7. 2019Neural Predictor for Neural Architecture Searchcited by 118
  8. 2020Can weight sharing outperform random architecture search? An investigation with TuNAScited by 68
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  10. 2020AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Predictioncited by 52
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  13. 2022On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Modelscited by 34
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  17. 2023DistDNAS: Search Efficient Feature Interactions within 2 Hourscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 2.

3 papers.

  1. 2024Finite-Time Convergence and Sample Complexity of Actor-Critic Multi-Objective Reinforcement Learningcited by 4
  2. 2026Design Once, Deploy at Scale: Template-Driven ML Development for Large Model Ecosystemscited by 0
  3. 2026Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Searchcited by 0