Batching the Reflective Optimization Loop: Parallel Proposals Make GEPA Faster and Better

Authors: Jialin Zhang , Lakshya A Agrawal , Shangyin Tan , Donghyun Lee , Lutfi Eren Erdogan , Dan Klein , Koushik Sen , Alex Dimakis , Matei Zaharia

View a PDF of the paper titled Batching the Reflective Optimization Loop: Parallel Proposals Make GEPA Faster and Better, by Jialin Zhang and 8 other authors

View PDF

HTML (experimental)

Abstract: Reflective text optimization makes agentic systems better, but the optimization loop is itself a resource-hungry workload whose sequential structure leaves parallel evaluation capacity idle. We extend the open-source GEPA optimizer to propose and evaluate a batch of candidates on each optimization step instead of one candidate at a time. In our sweep on two tasks, most batched runs finished in half the wall-clock time or less, and the fastest in about a quarter to a third. Batched settings also achieved higher held-out test scores, from 68.9% to 72.1% on LiveBench-Math (with $2\times2$) and from 49.0% to 60.0% on HoVer (with $8\times1$). A simple runtime model explains the measured speedups under a finite worker pool, and batched settings also spend their metric-call and dollar budgets more efficiently. We frame the axes along which a reflective optimization step can scale in parallel, a first look at allocating a fixed optimization budget across them.

Comments:

NeurIPS 2026 Workshop on Resource-Aware Agentic AI. 9 pages, 7 figures. First four authors contributed equally. Code: this https URL

Subjects:

Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL); Machine Learning (cs.LG)

Cite as:

arXiv:2610.36774 [cs.AI]

(or

arXiv:2610.36774v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.36774

Focus to learn more

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Jialin Zhang [ view email ]

[v1]

Tue, 27 Oct 2026 07:41:40 UTC (211 KB)

Full-text links:

Access Paper:

View a PDF of the paper titled Batching the Reflective Optimization Loop: Parallel Proposals Make GEPA Faster and Better, by Jialin Zhang and 8 other authors

View PDF

HTML (experimental)

TeX Source

view license

Additional Features

Audio Summary

Current browse context:

cs.AI

< prev

next >

new

recent

| 2026-10

Change to browse by:

cs

cs.CL

cs.LG

References & Citations

SciX

Google Scholar

Semantic Scholar

export BibTeX citation

Loading...

BibTeX formatted citation

loading...

Data provided by:

Bookmark

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer ( What is the Explorer? )

Connected Papers Toggle

Connected Papers ( What is Connected Papers? )

Litmaps Toggle

Litmaps ( What is Litmaps? )

scite.ai Toggle

scite Smart Citations ( What are Smart Citations? )

Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv ( What is alphaXiv? )

Links to Code Toggle

CatalyzeX Code Finder for Papers ( What is CatalyzeX? )

DagsHub Toggle

DagsHub ( What is DagsHub? )

GotitPub Toggle

Gotit.pub ( What is GotitPub? )

Huggingface Toggle

Hugging Face ( What is Huggingface? )

ScienceCast Toggle

ScienceCast ( What is ScienceCast? )

Demos

Demos

Replicate Toggle

Replicate ( What is Replicate? )

Spaces Toggle

Hugging Face Spaces ( What is Spaces? )

Spaces Toggle

TXYZ.AI ( What is TXYZ.AI? )

Related Papers

Recommenders and Search Tools

Link to Influence Flower

Influence Flower ( What are Influence Flowers? )

Core recommender toggle

CORE Recommender ( What is CORE? )

Author

Venue

Institution

Topic

About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Which authors of this paper are endorsers? |

Disable MathJax ( What is MathJax? )