---
title: "Foundation Models for Decision Making: Problems, Methods, and"
description: "This survey addresses a growing gap. 226 references, cited by 41 papers here. Among them: 51."
authors: ["Sherry Yang", "Ofir Nachum", "Yilun Du", "Jason Wei", "Pieter Abbeel", "Dale Schuurmans"]
year: 2023
venue: "arXiv:2303.04129"
source: "https://arxiv.org/abs/2303.04129"
doi: "10.48550/arxiv.2303.04129"
arxiv: "2303.04129"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/yang-2023-foundation-models-decision-making-problems/"
---
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# Foundation Models for Decision Making: Problems, Methods, and Opportunities

**Authors:** [Sherry Yang](https://md.research.elamur.ai/free/en/authors/sherry-yang/), Ofir Nachum, [Yilun Du](https://md.research.elamur.ai/free/en/authors/yilun-du/), [Jason Wei](https://md.research.elamur.ai/free/en/authors/jason-wei/), [Pieter Abbeel](https://md.research.elamur.ai/free/en/authors/pieter-abbeel/) and [Dale Schuurmans](https://md.research.elamur.ai/free/en/authors/dale-schuurmans/) · **Year:** 2023 · **Venue:** arXiv:2303.04129 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2303.04129) · [PDF](https://arxiv.org/pdf/2303.04129) · [DOI 10.48550/arxiv.2303.04129](https://doi.org/10.48550/arxiv.2303.04129) · [arXiv 2303.04129](https://arxiv.org/abs/2303.04129) · [OpenAlex](https://openalex.org/W4323651349)

**Citations:** 51 citations (OpenAlex, 2026-10-14) · [41](https://md.research.elamur.ai/free/en/papers/yang-2023-foundation-models-decision-making-problems/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
This survey addresses a growing gap. Large pretrained models for vision and language are now used in settings where they must act, receive feedback, and plan over many steps. Sequential decision making, by contrast, has mostly trained agents from scratch on single tasks. The authors (Google Research Brain Team, UC Berkeley, MIT, University of Alberta) argue that the two fields benefit from being studied together. Agents could borrow world knowledge from broad pretraining to learn faster and generalize better. Foundation models could gain from interactive feedback and long-horizon planning. Using the MDP formalism, imitation learning and reinforcement learning as background, the report groups existing work by the role a foundation model plays:
- a generative model of behavior or of the environment;
- a learner of representations for states, actions, rewards and dynamics;
- in the case of language models, an agent or an environment in its own right (dialogue, reasoning, tool use).

It contributes no new dataset or benchmark. Its value lies in a shared conceptual map and a catalogue of open problems.

## Key points
- The paper is a perspective/survey (arXiv:2303.04129, March 2023). The ways of adapting foundation models that it discusses include prompting, RL, optimal control, planning and generative modeling under conditioning.
- It motivates the topic with the poor sample efficiency of tabula rasa RL. One cited example needs about 7 GPU days of game-play to solve a single Atari game.
- It reviews language models that call external tools: search engines, calculators, translators, MuJoCo simulators and program interpreters. It also covers RL from human feedback for dialogue agents.
- It lists open challenges:
  - mismatched data modalities, such as robot actions that are missing from web data;
  - unclear environment and task structure;
  - composing models across modalities;
  - grounding outputs in real-world physics without a simulator;
  - moving from offline to online RL finetuning.
- It notes ways to extract desired behavior without RL objectives, such as conditioning on goals or returns, chain-of-thought supervision, and training on sequences of improving behavior. It also proposes treating software tools as scalable online environments.

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

## Institutions

- [Google](https://md.research.elamur.ai/free/en/companies/google/) — Company, Mountain View, United States · 533 works · 443,884 citations
- [University of California, Berkeley](https://md.research.elamur.ai/free/en/universities/university-of-california-berkeley/) — University, Berkeley, United States · 243 works · 251,774 citations
- [Massachusetts Institute of Technology](https://md.research.elamur.ai/free/en/universities/massachusetts-institute-of-technology/) — University, Cambridge, United States · 274 works · 82,013 citations
- [University of Alberta](https://md.research.elamur.ai/free/en/universities/university-of-alberta/) — University, Edmonton, Canada · 54 works · 5,242 citations

📚 Shelves (3): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 19,503 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 18,805 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 3,621 works

🧭 Topics (1): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 4,145 works

## Cite

Cite as: Sherry Yang, Ofir Nachum, Yilun Du, Jason Wei, Pieter Abbeel and Dale Schuurmans. 2023. Foundation Models for Decision Making: Problems, Methods, and Opportunities. arXiv:2303.04129.

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

```bibtex
@misc{yang2023foundation,
  title = {{Foundation Models for Decision Making: Problems, Methods, and Opportunities}},
  author = {Sherry Yang, Ofir Nachum, Yilun Du, Jason Wei, Pieter Abbeel and Dale Schuurmans},
  year = {2023},
  doi = {10.48550/arxiv.2303.04129},
  eprint = {2303.04129},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2303.04129}
}
```

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## References

226 references, 55 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 3 November 2026*