# Launchpad: A Programming Model for Distributed Machine Learning Research

arXiv: https://arxiv.org/abs/2106.04516 · HTML: https://mlsanity.com/paper/2106.04516/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 7 Jun 2021.

## Builds on (most cited first, 9 of 9)

- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures](https://mlsanity.com/paper/1802.01561/) (2018)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Ray: A Distributed Framework for Emerging AI Applications](https://mlsanity.com/paper/1712.05889/) (2017)
- [Caffe: Convolutional Architecture for Fast Feature Embedding](https://mlsanity.com/paper/1408.5093/) (2014)
- [Acme: A Research Framework for Distributed Reinforcement Learning](https://mlsanity.com/paper/2006.00979/) (2020)
- [Reverb: A Framework For Experience Replay](https://mlsanity.com/paper/2102.04736/) (2021)
- [RLgraph: Modular Computation Graphs for Deep Reinforcement Learning](https://mlsanity.com/paper/1810.09028/) (2018)

## Built on by (most cited first, 14 of 14)

- [Reinforced Self-Training (ReST) for Language Modeling](https://mlsanity.com/paper/2308.08998/) (2023)
- [VeLO: Training Versatile Learned Optimizers by Scaling Up](https://mlsanity.com/paper/2211.09760/) (2022)
- [Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding](https://mlsanity.com/paper/2312.05328/) (2023)
- [Sample-Efficient Alignment for LLMs](https://mlsanity.com/paper/2411.01493/) (2024)
- [Temporally Abstract Partial Models](https://mlsanity.com/paper/2108.03213/) (2021)
- [SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores](https://mlsanity.com/paper/2306.16688/) (2023)
- [Learning Robust Real-Time Cultural Transmission without Human Data](https://mlsanity.com/paper/2203.00715/) (2022)
- [MAD for Robust Reinforcement Learning in Machine Translation](https://mlsanity.com/paper/2207.08583/) (2022)
- [Phantom -- A RL-driven multi-agent framework to model complex systems](https://mlsanity.com/paper/2210.06012/) (2022)
- [Co-Learning Empirical Games and World Models](https://mlsanity.com/paper/2305.14223/) (2023)
- [A Reference Architecture of Reinforcement Learning Frameworks](https://mlsanity.com/paper/2603.06413/) (2026)
- [Pavlovian Signalling with General Value Functions in Agent-Agent Temporal Decision Making](https://mlsanity.com/paper/2201.03709/) (2022)
- [The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agents](https://mlsanity.com/paper/2203.09498/) (2022)
- [marl-jax: Multi-Agent Reinforcement Leaning Framework](https://mlsanity.com/paper/2303.13808/) (2023)

## Abstract (arXiv)

A major driver behind the success of modern machine learning algorithms has been their ability to process ever-larger amounts of data. As a result, the use of distributed systems in both research and production has become increasingly prevalent as a means to scale to this growing data. At the same time, however, distributing the learning process can drastically complicate the implementation of even simple algorithms. This is especially problematic as many machine learning practitioners are not well-versed in the design of distributed systems, let alone those that have complicated communication topologies. In this work we introduce Launchpad, a programming model that simplifies the process of defining and launching distributed systems that is specifically tailored towards a machine learning audience. We describe our framework, its design philosophy and implementation, and give a number of examples of common learning algorithms whose designs are greatly simplified by this approach.