Research map / Architectures and efficiency / Neural architecture search
EvoPrompting: Language Models for Code-Level Neural Architecture Search
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| search with EvoPrompting to discover new triplet representations instead of the standard Triplet-GMPNN | Triplet-GMPNN · CLRS Heapsort OOD | OOD accuracy: 31.0 -> 69.9 | Helped |
| search with EvoPrompting to discover new triplet representations | Triplet-GMPNN · CLRS Bubble Sort OOD | OOD accuracy: 67.7 -> 88.9 | Helped |
| search with EvoPrompting to discover new triplet representations | Triplet-GMPNN · CLRS BFS OOD | OOD accuracy: 99.7 -> 100.0 | No clear effect |
| remove prompt-tuning between rounds (ablation) | EvoPrompting · MNIST-1D | max fitness / Pareto frontier | Hurt |
| select parents randomly instead of by fitness (ablation) | EvoPrompting · MNIST-1D | max fitness | Hurt |
| add prompt-tuning to random-parents prompting (ablation) | naive few-shot prompting · MNIST-1D | fitness | No clear effect |
| evolutionary search with prompt-tuning over naive few-shot prompting (ablation) | PaLM 62B · MNIST-1D | Pareto frontier of test error vs model size | Helped |
Closest papers by meaning
- EvoOptiGraph: Weakness-Driven Coevolution via Graph-Based Structural Generation for Optimization Modeling (NeurIPS 2026 · similarity 0.88)
- How to Auto-optimize Prompts for Domain Tasks? Adaptive Prompting and Reasoning through Evolutionary Domain Knowledge Adaptation (NeurIPS 2025 · similarity 0.87)
- Simple Baselines are Competitive with Code Evolution (NeurIPS 2026 · similarity 0.86)
- Use Your INSTINCT: INSTruction optimization for LLMs usIng Neural bandits Coupled with Transformers (ICML 2024 · similarity 0.86)
- ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution (NeurIPS 2024 · similarity 0.86)
- Scaling Prompt Synthesis for Large Language Model Reasoning (ICML 2026 · similarity 0.86)
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