Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· 0 citations· 13 references
TL;DR
RecCompl is a comprehensive model compilation system that enables efficient model compilation of industrial scale DLRMs with PT2 and introduces a configuration-based user interface that decouples compilation settings from model code, allowing fine-grained control without intrusive changes.
Abstract
Deep Learning Recommendation Models (DLRMs) play a key role to power real-world recommendation and ranking, yet their growing complexity has made production deployment increasingly challenging. While PyTorch 2 (PT2) offers promising performance and productivity gains through automated model compilation, its initial release lacked critical features needed for DLRM adoption. In this work, we present RecCompl, a comprehensive model compilation system that enables efficient model compilation of industrial scale DLRMs with PT2. RecCompl addresses key compatibility issues by extending operator coverage, minimizing graph breaks, avoiding unnecessary recompilation, and generalizing graph transformation. Besides, to meet the high requirement on model exploration, we introduce a configuration-based user interface that decouples compilation settings from model code, allowing fine-grained control without intrusive changes. Despite these improvements, efficiency gaps remain in achieving a production-ready compilation system. To close them, we introduce systematic designs and engineering optimizations that enhance compilation time, memory management, and online deployment reliability. RecCompl is widely adopted, and delivers up to 60% higher training throughput while achieving substantially lower compilation latency, consistent performance across varying memory budgets, and stable online deployment.
The engineering details of ADEPT are presented, a framework that integrates representative adequacy techniques, including neuron-coverage-based metrics, surprise adequacy, input distribution coverage, boundary coverage, and source- and model-level mutation score, under a consistent execution workflow.
Yidi Kao, Shawn Burnham, Tommi Rose Fahy et al.· 0 citations
Generative retrieval has become an increasingly adopted paradigm for industrial search, recommendation, and advertising systems, delivering significant online gains. Most existing work combines user behavior sequences with large language models (LLMs) to model user preferences. In practice, feature engineering remains critical to model effectiveness, yet its complexity slows offline iteration and makes online deployment heavy and hard to reuse, all under tight online latency budgets. The root cause is a tight coupling between feature-processing logic and model architecture, where every feature change touches the training and serving code and resists reuse across scenarios. To break this coupling, we present Prompt Generation (PG), a high-level tokenizer and configuration-driven framework that decouples feature-processing logic from model architecture through two declarative JSON files, which serve as the single source of truth for both offline training and online serving, ensuring feature consistency across the two stages. Organizing features under four types with three composable processing components to assemble and compress heterogeneous features, PG delivers acceleration at three levels: (1)fast training iteration: feature experiments require only configuration changes, with built-in token compression for ultra-long sequences; (2)fast deployment: a new scenario only needs to conform to the PG schema and plug into a universal pipeline, with no scenario-specific engineering; (3)fast online inference: engine applies unified optimizations over the standardized configuration, reducing PG's overhead to a negligible level. PG has been deployed on Taobao Search with statistically significant online A/B uplifts of +0.47% in transaction count and +0.51% in GMV, and has been applied across multiple Taobao search and recommendation teams as the iteration framework for generative retrieval.
The design of transformer-based Large Language Models (LLMs) is being radically changed through new architectures that are able to overcome scalability limitations of previous designs, including Mixture-of-Experts (MoE), Multi-Head Latent Attention (MLA), and Multi-Token Prediction (MTP). As an open-weighted model released at the end of 2024, which has both state of the art architectural transparency and production scale efficiency, DeepSeeek-V3 represents the ultimate testing ground for investigating these modern technologies. This paper provides a comprehensive analysis of the architectural structure of DeepSeek-V3 based upon information from the DeepSeek-V3 Technical Report, industry benchmarking data and independent latency testing, to demonstrate how various techniques can be used to optimize training while still providing competitive performance in code generation and mathematical reasoning. In addition, latency testing conducted on a Distilled version of DeepSeek-V3, with approximately 14 billion parameters, running on a T4 GPU, reveals that although significant improvements have been made in optimizing latency there remains substantial barriers to deploying these models. Through this context, this research will serve as a reference document for practitioners and researchers who wish to understand current trends and challenges in increasing accessibility to high performance AI models.
Yassine Zouhdi, B. Hdioud· EPJ Web of Conferences· 0 citations
This work proposes Request-Oriented Compute Sharing (ROCS), a modeling and inference paradigm that exploits a unique property of recommendation inference: each user request is evaluated against many candidates, while request-side features are shared across candidates.
The rapid deployment of machine learning systems across cloud, edge, and enterprise environments has brought model optimization to the forefront of systems-engineering. Despite a rich literature spanning quantization, pruning, knowledge distillation, parameter-efficient fine-tuning (PEFT), and inference-time optimization, practitioners are often left navigating these techniques through heuristics rather than principled methodology. We argue that optimization should be formulated as a constraint-driven, multi-objective engineering decision and introduce a unified framework that characterizes any production deployment along five interacting constraint dimensions: data availability, latency budget, memory budget, accuracy tolerance, and retraining budget. Building on this taxonomy, we synthesize empirical gains reported across the research literature and map them to operational constraints rather than algorithmic categories. To ensure practical relevance, we selected these techniques by reviewing recent literature for methods that report measurable improvements against critical deployment bottlenecks. We propose a prescriptive decision framework and provide optimization pipelines for four representative industrial scenarios to illustrate it in practice. To the best of our knowledge, this work provides one of the first structured attempts to formalize model optimization as a constraint-aware, multi-objective engineering process, synthesizing quantitative evidence from the research literature.
Dhruv Shivkant, S. Mohanty, S. Rai et al.· 0 citations
This work introduces PromptPack, a scalable, high-throughput LLM annotation agent that cuts LLM costs by 89% and accelerates throughput by 2.5x while fully preserving AUC.
Sebastian Koralewski, Merwan Barlier, Yulia Stolin et al.· 0 citations
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