---
title: "DreamCoder — paper by Ellis et al. (2020) — summary and ideas"
description: "Program induction treats learning a task as finding a program that produces the desired behavior. 61 references, cited by 22 papers here."
authors: ["Kevin Ellis", "Catherine Wong", "Maxwell Nye", "Mathias Sable-Meyer", "Luc Cary", "Lucas Morales", "Luke Hewitt", "Armando Solar-Lezama", "Joshua B. Tenenbaum"]
year: 2020
venue: "arXiv:2006.08381"
source: "https://arxiv.org/abs/2006.08381"
doi: "10.48550/arxiv.2006.08381"
arxiv: "2006.08381"
licence: "arXiv — non-exclusive licence to distribute"
licence_url: "https://arxiv.org/licenses/nonexclusive-distrib/1.0/license.html"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/ellis-2020-dreamcoder-growing-generalizable-interpretable-knowledge/"
---
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# DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning

**Authors:** [Kevin Ellis](https://md.research.elamur.ai/free/en/authors/kevin-ellis/), [Catherine Wong](https://md.research.elamur.ai/free/en/authors/catherine-wong/), [Maxwell Nye](https://md.research.elamur.ai/free/en/authors/maxwell-nye/), [Mathias Sable-Meyer](https://md.research.elamur.ai/free/en/authors/mathias-sable-meyer/), Luc Cary, Lucas Morales, [Luke Hewitt](https://md.research.elamur.ai/free/en/authors/luke-hewitt/), [Armando Solar-Lezama](https://md.research.elamur.ai/free/en/authors/armando-solar-lezama/) and [Joshua B. Tenenbaum](https://md.research.elamur.ai/free/en/authors/joshua-b-tenenbaum/) · **Year:** 2020 · **Venue:** arXiv:2006.08381 · **Licence:** [arXiv — non-exclusive licence to distribute](https://arxiv.org/licenses/nonexclusive-distrib/1.0/license.html)

**Links:** [Publisher's page](https://arxiv.org/abs/2006.08381) · [PDF](https://arxiv.org/pdf/2006.08381) · [DOI 10.48550/arxiv.2006.08381](https://doi.org/10.48550/arxiv.2006.08381) · [arXiv 2006.08381](https://arxiv.org/abs/2006.08381)

**Citations:** [22](https://md.research.elamur.ai/free/en/papers/ellis-2020-dreamcoder-growing-generalizable-interpretable-knowledge/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Program induction treats learning a task as finding a program that produces the desired behavior. It generalizes well from a few examples and yields knowledge humans can read. In practice, though, it has relied on hand-crafted domain-specific languages (DSLs) and search procedures tuned to them. DreamCoder learns both instead. Starting from a sparse set of generic primitives, it cycles through three phases:
- **Wake:** a neural recognition model guides the search for programs that solve training tasks.
- **Abstraction sleep:** recurring fragments of the solutions found are extracted and added to a growing library, which acts as the prior over programs.
- **Dream sleep:** the network is retrained on replayed tasks and on "fantasy" tasks sampled from the library.

Across eight domains, including classic list-processing synthesis, picture drawing and scene building, the system builds layered, interpretable libraries in which new concepts reuse older ones. It also rediscovers core ideas of functional programming, vector algebra and classical physics. This points to program learning as a scalable, data-efficient way to accumulate reusable symbolic knowledge.

## Key points
- Learns two kinds of expertise together: an explicit library (the DSL, a Bayesian prior over programs) and implicit search skill (a neural network that approximates the posterior). It extends the authors' earlier EC² algorithm.
- In the list domain, cycling through 109 tasks produced a library of roughly 20 list operations. After six iterations, "Sort List" was solved with five calls to learned functions in under 10 minutes. The same program written in the initial primitives needs 32 calls and would take over 10^72 years of brute-force search.
- New tasks are typically specified by only 5–10 examples, or by a single image or scene.
- Among the physics results, it recovered Newton's and Coulomb's laws from minimal starting primitives.
- Compute cost is high: building the "origami" functional-programming library took about a year of total CPU time. The authors therefore favor richer built-in starting knowledge over learning from scratch.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

📚 Shelves (1): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 19,503 works

## Cite

Cite as: Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sable-Meyer, Luc Cary, Lucas Morales, Luke Hewitt, Armando Solar-Lezama and Joshua B. Tenenbaum. 2020. DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning. arXiv:2006.08381.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{ellis2020dreamcoder,
  title = {{DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning}},
  author = {Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sable-Meyer, Luc Cary, Lucas Morales, Luke Hewitt, Armando Solar-Lezama and Joshua B. Tenenbaum},
  year = {2020},
  doi = {10.48550/arxiv.2006.08381},
  eprint = {2006.08381},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2006.08381}
}
```

- [← previous A Pointer Network Architecture for Joint Morphological Segmentation and Tagging](https://md.research.elamur.ai/free/en/papers/seker-2020-pointer-network-architecture-joint-morphological/)
- [next → ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/xiao-2020-ernie-gram-pre-training/)
- [↑ back to papers of 2020](https://md.research.elamur.ai/free/en/papers/2020/page/8/)

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*Updated 3 November 2026*