---
title: "TextGrad: Automatic \"Differentiation\" via Text (2024)"
description: "The authors propose TextGrad, a framework that treats such a pipeline as a… 91.9% accuracy. 104 references, cited by 76 papers here."
authors: ["Mert Yuksekgonul", "Federico Bianchi", "Joseph Boen", "Sheng Liu", "Zhi Huang", "Carlos Guestrin", "James Zou"]
year: 2024
venue: "arXiv:2406.07496"
source: "https://arxiv.org/abs/2406.07496"
doi: "10.48550/arxiv.2406.07496"
arxiv: "2406.07496"
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/yuksekgonul-2024-textgrad-automatic-differentiation-text/"
---
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# TextGrad: Automatic "Differentiation" via Text

**Authors:** [Mert Yuksekgonul](https://md.research.elamur.ai/free/en/authors/mert-yuksekgonul/), [Federico Bianchi](https://md.research.elamur.ai/free/en/authors/federico-bianchi/), Joseph Boen, Sheng Liu, Zhi Huang, [Carlos Guestrin](https://md.research.elamur.ai/free/en/authors/carlos-guestrin/) and [James Zou](https://md.research.elamur.ai/free/en/authors/james-zou/) · **Year:** 2024 · **Venue:** arXiv:2406.07496 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2406.07496) · [PDF](https://arxiv.org/pdf/2406.07496) · [DOI 10.48550/arxiv.2406.07496](https://doi.org/10.48550/arxiv.2406.07496) · [arXiv 2406.07496](https://arxiv.org/abs/2406.07496) · [OpenAlex](https://openalex.org/W4399655920)

**Citations:** 12 citations (OpenAlex, 2026-10-14) · [76](https://md.research.elamur.ai/free/en/papers/yuksekgonul-2024-textgrad-automatic-differentiation-text/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Modern AI applications increasingly chain several LLMs with tools such as simulators, solvers, or search engines. These compound pipelines are usually tuned by hand, and their parts are not differentiable, so standard gradient training does not apply. The authors propose TextGrad, a framework that treats such a pipeline as a computation graph and borrows the idea of backpropagation. Instead of numerical gradients, an LLM writes natural-language critiques ("textual gradients") explaining how each variable should change to improve a downstream objective. These critiques are passed backward through arbitrary function calls, and an optimizer then rewrites the variables. The API deliberately mirrors PyTorch. Users only define the objective, with no task-specific prompt tuning. Unlike earlier prompt-focused systems such as DSPy and ProTeGi, TextGrad also optimizes individual solutions directly, including code, answers, molecules, and treatment plans. Experiments cover coding, scientific question answering, prompt optimization for reasoning, drug-like molecule design, and radiotherapy planning. The work matters because it offers one general, open-source way to automatically improve multi-component LLM systems.

## Key points
- On Google-Proof Question Answering (GPQA), refining answers at test time lifts GPT-4o zero-shot accuracy from 51% to 55%.
- On LeetCode-Hard coding problems, TextGrad gives about a 20% relative gain over gpt-4o and the best prior method.
- Prompt optimization brings GPT-3.5 close to GPT-4 on several reasoning tasks; one illustrated prompt rises from 77.8% to 91.9% accuracy.
- PyTorch-style abstractions include `tg.Variable`, `tg.BlackboxLLM`, `tg.TextLoss`, and the `tg.TGD` optimizer, with `loss.backward()` and `optimizer.step()` calls.
- The molecule and prostate-cancer radiotherapy results are proof-of-concept, validated only in silico (e.g., Vina scores). The code is released at github.com/zou-group/textgrad.

*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

- [Stanford University](https://md.research.elamur.ai/free/en/universities/stanford-university/) — University, Stanford, United States · 310 works · 284,480 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 9,086 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 8,889 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 9,296 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 9,206 works

## Cite

Cite as: Mert Yuksekgonul, Federico Bianchi, Joseph Boen, Sheng Liu, Zhi Huang, Carlos Guestrin and James Zou. 2024. TextGrad: Automatic "Differentiation" via Text. arXiv:2406.07496.

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

```bibtex
@misc{yuksekgonul2024textgrad,
  title = {{TextGrad: Automatic "Differentiation" via Text}},
  author = {Mert Yuksekgonul, Federico Bianchi, Joseph Boen, Sheng Liu, Zhi Huang, Carlos Guestrin and James Zou},
  year = {2024},
  doi = {10.48550/arxiv.2406.07496},
  eprint = {2406.07496},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2406.07496}
}
```

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*Updated 24 October 2026*