---
title: "MLR-Copilot — paper by Li et al. (2024) — summary and ideas"
description: "MLR-Copilot is an LLM-agent framework for automating the full machine learning research cycle. 41 references, cited by 40 papers here."
authors: ["Ruochen Li", "Teerth Patel", "Qingyun Wang", "Xinya Du"]
year: 2024
venue: "arXiv:2408.14033"
source: "https://arxiv.org/abs/2408.14033"
doi: "10.48550/arxiv.2408.14033"
arxiv: "2408.14033"
licence: "arXiv non-exclusive distribution license"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/li-2024-mlr-copilot-autonomous-machine/"
---
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# MLR-Copilot: Autonomous Machine Learning Research based on Large Language Models Agents

**Authors:** [Ruochen Li](https://md.research.elamur.ai/free/en/authors/ruochen-li/), [Teerth Patel](https://md.research.elamur.ai/free/en/authors/teerth-patel/), [Qingyun Wang](https://md.research.elamur.ai/free/en/authors/qingyun-wang/) and [Xinya Du](https://md.research.elamur.ai/free/en/authors/xinya-du/) · **Year:** 2024 · **Venue:** arXiv:2408.14033 · **Licence:** [arXiv non-exclusive distribution license](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2408.14033) · [PDF](https://arxiv.org/pdf/2408.14033) · [DOI 10.48550/arxiv.2408.14033](https://doi.org/10.48550/arxiv.2408.14033) · [arXiv 2408.14033](https://arxiv.org/abs/2408.14033) · [OpenAlex](https://openalex.org/W4402952811)

**Citations:** 3 citations (OpenAlex, 2026-10-14) · [40](https://md.research.elamur.ai/free/en/papers/li-2024-mlr-copilot-autonomous-machine/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
MLR-Copilot is an LLM-agent framework for automating the full machine learning research cycle. Earlier auto-experimentation systems began with a predefined task and a ready code template. This system starts from a published paper instead. It runs in three stages. First, IdeaAgent takes selected sections of the input paper (via the Semantic Scholar API) and extracts the task, research gaps and keywords. It then gathers recent related work and proposes a methodology with an experiment plan. IdeaAgent was trained with supervised fine-tuning and then fine-grained RL, using reward models for novelty, feasibility and effectiveness built from OpenReview review data. Second, ExperimentAgent adapts prototype code from the source paper and can optionally fetch models and datasets from HuggingFace. Third, it runs the experiments, debugs them over several iterations and can accept human feedback. The authors evaluate the system manually and automatically on five ML research tasks and report novel, feasible ideas with executable results. The supplied excerpt omits the detailed scores. The work is an early end-to-end research copilot, released in August 2024 alongside AI Scientist.

## Key points
- Pipeline: idea generation (IdeaAgent) → experiment implementation → implementation execution (both handled by ExperimentAgent), with execution, debugging and optional human feedback looping back into the implementation.
- IdeaAgent training data: 4,271 ICLR/NeurIPS papers from 2023–2024, collected via OpenReview. Supervised fine-tuning used a derived set of 1,000 papers with extracted ideas and plans. RL used rewards for novelty, feasibility and effectiveness, with no steering at inference time.
- A research idea is represented as RI = {P, R, h, e}: source-paper information, retrieved recent work, generated methodology and experiment plan.
- Evaluation covers five ML research tasks, including a sentiment-analysis case study on the ELLIPSE dataset. Figure 1 shows an example execution result of 0.787 on a semantic textual similarity task.
- Code, data and models are at github.com/du-nlp-lab/MLR-Copilot, and a GUI demo is on HuggingFace Spaces (du-lab/MLR-Copilot). The authors note a limitation: the stages are mostly sequential, with little ability to go back from execution to ideation.

*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

- [University of Illinois Urbana-Champaign](https://md.research.elamur.ai/free/en/universities/university-of-illinois-urbana-champaign/) — University, Urbana, United States · 228 works · 74,591 citations
- [The University of Texas at Dallas](https://md.research.elamur.ai/free/en/universities/the-university-of-texas-at-dallas/) — University, Richardson, United States · 66 works · 5,836 citations

📚 Shelves (3): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 19,503 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 18,805 works · [🗄️ Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · 1,888 works

## Cite

Cite as: Ruochen Li, Teerth Patel, Qingyun Wang and Xinya Du. 2024. MLR-Copilot: Autonomous Machine Learning Research based on Large Language Models Agents. arXiv:2408.14033.

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

```bibtex
@misc{li2024mlr,
  title = {{MLR-Copilot: Autonomous Machine Learning Research based on Large Language Models Agents}},
  author = {Ruochen Li, Teerth Patel, Qingyun Wang and Xinya Du},
  year = {2024},
  doi = {10.48550/arxiv.2408.14033},
  eprint = {2408.14033},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2408.14033}
}
```

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## References

41 references, 24 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 3 November 2026*