# Market-based Architectures in RL and Beyond

arXiv: https://arxiv.org/abs/2503.05828 · HTML: https://mlsanity.com/paper/2503.05828/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 5 Mar 2025.

## Builds on (most cited first, 7 of 7)

- [Towards Reasoning in Large Language Models: A Survey](https://mlsanity.com/paper/2212.10403/) (2022)
- [Adaptive Computation Time for Recurrent Neural Networks](https://mlsanity.com/paper/1603.08983/) (2016)
- [Dynamic Neural Networks: A Survey](https://mlsanity.com/paper/2102.04906/) (2021)
- [Logical Induction](https://mlsanity.com/paper/1609.03543/) (2016)
- [Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions](https://mlsanity.com/paper/2007.02382/) (2020)
- [Language Models Can Reduce Asymmetry in Information Markets](https://mlsanity.com/paper/2403.14443/) (2024)
- [A Theory of Bounded Inductive Rationality](https://mlsanity.com/paper/2307.05068/) (2023)

## Built on by (most cited first, 1 of 1)

- [Economy of Minds: Emerging Multi-Agent Intelligence with Economic Interactions](https://mlsanity.com/paper/2606.02859/) (2026)

## Abstract (arXiv)

Market-based agents refer to reinforcement learning agents which determine their actions based on an internal market of sub-agents. We introduce a new type of market-based algorithm where the state itself is factored into several axes called ``goods'', which allows for greater specialization and parallelism than existing market-based RL algorithms. Furthermore, we argue that market-based algorithms have the potential to address many current challenges in AI, such as search, dynamic scaling and complete feedback, and demonstrate that they may be seen to generalize neural networks; finally, we list some novel ways that market algorithms may be applied in conjunction with Large Language Models for immediate practical applicability.