# Can You Improve My Code? Optimizing Programs with Local Search

arXiv: https://arxiv.org/abs/2307.05603 · HTML: https://mlsanity.com/paper/2307.05603/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 44 references that are in the map.
- First posted on arXiv 10 Jul 2023.

## Builds on (most cited first, 13 of 13)

- [Programmatically Interpretable Reinforcement Learning](https://mlsanity.com/paper/1804.02477/) (2018)
- [Verifiable Reinforcement Learning via Policy Extraction](https://mlsanity.com/paper/1805.08328/) (2018)
- [DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning](https://mlsanity.com/paper/2006.08381/) (2020)
- [DeepCoder: Learning to Write Programs](https://mlsanity.com/paper/1611.01989/) (2016)
- [RobustFill: Neural Program Learning under Noisy I/O](https://mlsanity.com/paper/1703.07469/) (2017)
- [Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples](https://mlsanity.com/paper/1804.01186/) (2018)
- [A Survey on Machine Learning Techniques for Source Code Analysis](https://mlsanity.com/paper/2110.09610/) (2021)
- [Learning to Complete Code with Sketches](https://mlsanity.com/paper/2106.10158/) (2021)
- [Just-in-Time Learning for Bottom-Up Enumerative Synthesis](https://mlsanity.com/paper/2010.08663/) (2020)
- [Synthetic Datasets for Neural Program Synthesis](https://mlsanity.com/paper/1912.12345/) (2019)
- [What can we Learn Even From the Weakest? Learning Sketches for Programmatic Strategies](https://mlsanity.com/paper/2203.11912/) (2022)
- [The Effectiveness of Supervised Machine Learning Algorithms in Predicting Software Refactoring](https://mlsanity.com/paper/2001.03338/) (2020)
- [Improving Reinforcement Learning with Human Assistance: An Argument for Human Subject Studies with HIPPO Gym](https://mlsanity.com/paper/2102.02639/) (2021)

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

- [Learning Game-Playing Agents with Generative Code Optimization](https://mlsanity.com/paper/2508.19506/) (2025)

## Abstract (arXiv)

This paper introduces a local search method for improving an existing program with respect to a measurable objective. Program Optimization with Locally Improving Search (POLIS) exploits the structure of a program, defined by its lines. POLIS improves a single line of the program while keeping the remaining lines fixed, using existing brute-force synthesis algorithms, and continues iterating until it is unable to improve the program's performance. POLIS was evaluated with a 27-person user study, where participants wrote programs attempting to maximize the score of two single-agent games: Lunar Lander and Highway. POLIS was able to substantially improve the participants' programs with respect to the game scores. A proof-of-concept demonstration on existing Stack Overflow code measures applicability in real-world problems. These results suggest that POLIS could be used as a helpful programming assistant for programming problems with measurable objectives.