@sherryyangML: Machine learning engineering (MLE) is the new agentic frontier. I'll be sharing our work on scaling RL for MLE agents a…
Summary
Two ICLR 2026 papers show how small RL-trained agents outperform frontier models on machine-learning engineering tasks and how MLE-Smith automatically scales MLE workloads.
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MLExpert: Towards a Generalist Agentic Model for Autonomous Machine Learning Engineering
提出了 MLExpert，一个 35B 混合专家（MoE）智能体模型，通过分层监督微调和窗口化交互强化学习（WIRL）自主优化长周期机器学习工程工作流，在 MLE-Bench Lite 上将基模型奖牌率从 37.9% 提升至 72.7%，媲美更大的前沿模型。
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A new research paper reframes LLM agent training as a cross-episode Meta-RL problem, using critic-free policy gradients to enable in-context adaptation without gradient updates. The LAMER framework improves test-time performance by 11-19% over standard RL baselines on long-horizon tasks and generalizes better to unseen environments.
MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering
OpenAI introduces MLE-bench, a benchmark of 75 Kaggle ML competitions to evaluate AI agents on real-world ML engineering tasks. The best setup, o1-preview with AIDE scaffolding, achieves at least a Kaggle bronze medal in 16.9% of competitions.
@rohanpaul_ai: Meta published A-MLE, a new AI agent framework they actually built and deployed to automate ML experimentation across t…
Meta has developed and deployed A-MLE, an AI agent framework that automates ML experimentation for their ads-ranking models, showing significant performance gains over generic LLMs.
@sheriyuo: Meta-RL really does feel like a very promising direction
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