mlsanityData to 7 Oct 2026

Learning to Ideate for Machine Learning Engineering Agents

arXiv 2601.17596 ↗First posted 24 Jan 2026cs.CLcited by 6

Not on the map. Why?

Abstract (arXiv)

Existing machine learning engineering (MLE) agents struggle to iteratively optimize their implemented algorithms for effectiveness. To address this, we introduce MLE-Ideator, a dual-agent framework that separates ideation from implementation. In our system, an implementation agent can request strategic help from a dedicated Ideator. We show this approach is effective in two ways. First, in a training-free setup, our framework significantly outperforms implementation-only agent baselines on MLE-Bench. Second, we demonstrate that the Ideator can be trained with reinforcement learning (RL) to generate more effective ideas. With only 1K training samples from 10 MLE tasks, our RL-trained Qwen3-8B Ideator achieves an 11.5% relative improvement compared to its untrained counterpart and surpasses Claude Sonnet 3.5. These results highlights a promising path toward training strategic AI systems for scientific discovery.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 19 of its 28 references that are in the map. First posted on arXiv 24 Jan 2026.

Builds on

19 of 28 references.

References · 19

  1. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
  2. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  3. 2024HybridFlow: A Flexible and Efficient RLHF Frameworkcited by 1,980
  4. 2024Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameterscited by 1,928
  5. 2024The AI Scientist: Towards Fully Automated Open-Ended Scientific Discoverycited by 1,002
  6. 2023Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervisioncited by 445
  7. 2025Agent Laboratory: Using LLM Agents as Research Assistantscited by 405
  8. 2023MLAgentBench: Evaluating Language Agents on Machine Learning Experimentationcited by 339
  9. 2025AIDE: AI-Driven Exploration in the Space of Codecited by 206
  10. 2025Scaling Test-time Compute for LLM Agentscited by 59
  11. 2025ML-Master: Towards AI-for-AI via Integration of Exploration and Reasoningcited by 57
  12. 2025Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interactioncited by 34
7 more
  1. 2025MLRC-Bench: Can Language Agents Solve Machine Learning Research Challenges?cited by 27
  2. 2025ML-Agent: Reinforcing LLM Agents for Autonomous Machine Learning Engineeringcited by 26
  3. 2025MLZero: A Multi-Agent System for End-to-end Machine Learning Automationcited by 22
  4. 2024ProxyLM: Predicting Language Model Performance on Multilingual Tasks via Proxy Modelscited by 20
  5. 2025RepoMaster: Autonomous Exploration and Understanding of GitHub Repositories for Complex Task Solvingcited by 19
  6. 2025Reinforcement Learning for Machine Learning Engineering Agentscited by 13
  7. 2025Bootstrapping Task Spaces for Self-Improvementcited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 6.

6 papers.

  1. 2026AI Research Preference Modelscited by 7
  2. 2026AdaMEM: Test-Time Adaptive Memory for Language Agentscited by 4
  3. 2026ScienceFlow: A long-horizon agent for ML research, scientific discovery and beyondcited by 3
  4. 2026Can LLM Agents Discover? Evaluating Creativity on ML Engineering Taskscited by 1
  5. 2026VTOS: Learning to Orchestrate Vision Tools by Co-Searching Solutions and Observerscited by 0
  6. 2026How Much of a Harness Does a Strong Agent Need for Autonomous ML Engineering?cited by 0