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🧠 AI🟢 BullishImportance 6/10
Reasoning as Gradient: Scaling MLE Agents Beyond Tree Search
arXiv – CS AI|Yifei Zhang, Xu Yang, Xiao Yang, Bowen Xian, Qizheng Li, Shikai Fang, Jingyuan Li, Jian Wang, Mingrui Xu, Weiqing Liu, Jiang Bian||8 views
🤖AI Summary
Researchers introduced GOME, an AI agent that uses gradient-based optimization instead of tree search for machine learning engineering tasks, achieving 35.1% success rate on MLE-Bench. The study shows gradient-based approaches outperform tree search as AI reasoning capabilities improve, suggesting this method will become more effective as LLMs advance.
Key Takeaways
- →GOME agent achieved state-of-the-art 35.1% any-medal rate on MLE-Bench using gradient-based optimization instead of traditional tree search.
- →Gradient-based optimization becomes increasingly superior to tree search as LLM reasoning capabilities strengthen.
- →Weaker models still benefit from tree search due to unreliable reasoning requiring exhaustive exploration.
- →The research positions gradient-based optimization as the preferred paradigm for advanced reasoning-oriented LLMs.
- →The study tested across 10 different models and released codebase with GPT-5 traces for reproducibility.
#machine-learning#llm-agents#gradient-optimization#tree-search#mle-bench#ai-research#reasoning#gpt-5#arxiv
Read Original →via arXiv – CS AI
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