# AlphaD3M: Machine Learning Pipeline Synthesis

arXiv: https://arxiv.org/abs/2111.02508 · HTML: https://mlsanity.com/paper/2111.02508/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 3 Nov 2021.

## Builds on (most cited first, 8 of 8)

- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [Thinking Fast and Slow with Deep Learning and Tree Search](https://mlsanity.com/paper/1705.08439/) (2017)
- [PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison](https://mlsanity.com/paper/1703.00512/) (2017)
- [Generating Sentences by Editing Prototypes](https://mlsanity.com/paper/1709.08878/) (2017)
- [Autostacker: A Compositional Evolutionary Learning System](https://mlsanity.com/paper/1803.00684/) (2018)
- [Solving the Rubik's Cube Without Human Knowledge](https://mlsanity.com/paper/1805.07470/) (2018)

## Built on by (most cited first, 24 of 24)

- [Data-centric Artificial Intelligence: A Survey](https://mlsanity.com/paper/2303.10158/) (2023)
- [AMLB: an AutoML Benchmark](https://mlsanity.com/paper/2207.12560/) (2022)
- [AutoPDL: Automatic Prompt Optimization for LLM Agents](https://mlsanity.com/paper/2504.04365/) (2025)
- [DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programs](https://mlsanity.com/paper/2205.01938/) (2022)
- [DreamShard: Generalizable Embedding Table Placement for Recommender Systems](https://mlsanity.com/paper/2210.02023/) (2022)
- [XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine Learning](https://mlsanity.com/paper/2202.11954/) (2022)
- [SubStrat: A Subset-Based Strategy for Faster AutoML](https://mlsanity.com/paper/2206.03070/) (2022)
- [Task Selection for AutoML System Evaluation](https://mlsanity.com/paper/2208.12754/) (2022)
- [SPIO: Ensemble and Selective Strategies via LLM-Based Multi-Agent Planning in Automated Data Science](https://mlsanity.com/paper/2503.23314/) (2025)
- [LLM-Guided Evolution: An Autonomous Model Optimization for Object Detection](https://mlsanity.com/paper/2504.02280/) (2025)
- [Review of the state of the art in autonomous artificial intelligence](https://mlsanity.com/paper/2210.10659/) (2022)
- [Statistical Test for Feature Selection Pipelines by Selective Inference](https://mlsanity.com/paper/2406.18902/) (2024)
- [Deep Pipeline Embeddings for AutoML](https://mlsanity.com/paper/2305.14009/) (2023)
- [Tree-Structured Synergy of Large Language Models and Bayesian Optimization for Efficient CASH](https://mlsanity.com/paper/2601.12355/) (2026)
- [Towards Personalized Preprocessing Pipeline Search](https://mlsanity.com/paper/2302.14329/) (2023)
- [auto-sktime: Automated Time Series Forecasting](https://mlsanity.com/paper/2312.08528/) (2023)
- [Pulling the Carpet Below the Learner's Feet: Genetic Algorithm To Learn Ensemble Machine Learning Model During Concept Drift](https://mlsanity.com/paper/2412.09035/) (2024)
- [Efficient End-to-End AutoML via Scalable Search Space Decomposition](https://mlsanity.com/paper/2206.09423/) (2022)
- [Integrating Hyperparameter Search into Model-Free AutoML with Context-Free Grammars](https://mlsanity.com/paper/2404.03419/) (2024)
- [CoFEH: LLM-driven Feature Engineering Empowered by Collaborative Bayesian Hyperparameter Optimization](https://mlsanity.com/paper/2602.09851/) (2026)
- [Integrating Meta-Features with Knowledge Graph Embeddings for Meta-Learning](https://mlsanity.com/paper/2603.19888/) (2026)
- [Model-Aware Data Cleaning for Tabular Foundation Models](https://mlsanity.com/paper/2604.25154/) (2026)
- [Candidate-Fate Accounting for Transparent Sensor Diagnostic Pipeline Search](https://mlsanity.com/paper/2608.18665/) (2026)
- [SAGE-Loop: Reliable Closed-Loop LLM-Driven AutoML with Trial-and-Correction and Adaptive Ensembling](https://mlsanity.com/paper/2609.12455/) (2026)

## Abstract (arXiv)

We introduce AlphaD3M, an automatic machine learning (AutoML) system based on meta reinforcement learning using sequence models with self play. AlphaD3M is based on edit operations performed over machine learning pipeline primitives providing explainability. We compare AlphaD3M with state-of-the-art AutoML systems: Autosklearn, Autostacker, and TPOT, on OpenML datasets. AlphaD3M achieves competitive performance while being an order of magnitude faster, reducing computation time from hours to minutes, and is explainable by design.