# Ideal Partition of Resources for Metareasoning

arXiv: https://arxiv.org/abs/2110.09624 · HTML: https://mlsanity.com/paper/2110.09624/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 18 Oct 2021.

## Builds on (most cited first, 2 of 2)

- [Reasoning About Beliefs and Actions Under Computational Resource Constraints](https://mlsanity.com/paper/1304.2759/) (2013)
- [Bounded Conditioning: Flexible Inference for Decisions under Scarce Resources](https://mlsanity.com/paper/1304.1512/) (2013)

## Built on by (most cited first, 2 of 2)

- [Flows: Building Blocks of Reasoning and Collaborating AI](https://mlsanity.com/paper/2308.01285/) (2023)
- [From Medprompt to o1: Exploration of Run-Time Strategies for Medical Challenge Problems and Beyond](https://mlsanity.com/paper/2411.03590/) (2024)

## Abstract (arXiv)

We can achieve significant gains in the value of computation by metareasoning about the nature or extent of base-level problem solving before executing a solution. However, resources that are irrevocably committed to metareasoning are not available for executing a solution. Thus, it is important to determine the portion of resources we wish to apply to metareasoning and control versus to the execution of a solution plan. Recent research on rational agency has highlighted the importance of limiting the consumption of resources by metareasoning machinery. We shall introduce the metareasoning-partition problem--the problem of ideally apportioning costly reasoning resources to planning a solution versus applying resource to executing a solution to a problem. We exercise prototypical metareasoning-partition models to probe the relationships between time allocated to metareasoning and to execution for different problem classes. Finally, we examine the value of metareasoning in the context of our functional analyses.