Compute Allocation in Evolutionary Search: From Depth-Breadth to Multi-Armed Bandits
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866911730680987648 |
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| author | Xing, Sixue He, Haoyu Wu, Kerui Yang, Zhuo Luo, Haozheng Fu, Tianfan Nagarajan, Aarthy |
| author_facet | Xing, Sixue He, Haoyu Wu, Kerui Yang, Zhuo Luo, Haozheng Fu, Tianfan Nagarajan, Aarthy |
| contents | LLM-guided evolutionary search (Evolve systems) has reached state-of-the-art results on mathematical and combinatorial tasks, yet most existing systems report only the best of many runs and leave the run-to-run distribution undocumented. We ask how a fixed budget of LLM calls should be allocated, and how reliably a single run reaches the reported numbers. Sweeping the depth-breadth grid over five models and three tasks, we identify two empirical regularities: a fitness-compute envelope along which capability ordering largely collapses on effective FLOPs, and a bilinear depth-breadth fit with task-specific interaction; both are gated by model-task capability. Motivated by these regularities, we propose BaSE (Bandit-based Self-Evolving), a multi-armed bandit that allocates LLM calls across parallel trajectories. Without changing the model, prompt, or evaluator, BaSE improves mean fitness by 12.3% over the strongest island-protocol baseline across 8 (model, task) cells, with the largest gains on high-variance settings: a reliability gain from allocation alone. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_29268 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Compute Allocation in Evolutionary Search: From Depth-Breadth to Multi-Armed Bandits Xing, Sixue He, Haoyu Wu, Kerui Yang, Zhuo Luo, Haozheng Fu, Tianfan Nagarajan, Aarthy Computation and Language Artificial Intelligence Machine Learning Neural and Evolutionary Computing LLM-guided evolutionary search (Evolve systems) has reached state-of-the-art results on mathematical and combinatorial tasks, yet most existing systems report only the best of many runs and leave the run-to-run distribution undocumented. We ask how a fixed budget of LLM calls should be allocated, and how reliably a single run reaches the reported numbers. Sweeping the depth-breadth grid over five models and three tasks, we identify two empirical regularities: a fitness-compute envelope along which capability ordering largely collapses on effective FLOPs, and a bilinear depth-breadth fit with task-specific interaction; both are gated by model-task capability. Motivated by these regularities, we propose BaSE (Bandit-based Self-Evolving), a multi-armed bandit that allocates LLM calls across parallel trajectories. Without changing the model, prompt, or evaluator, BaSE improves mean fitness by 12.3% over the strongest island-protocol baseline across 8 (model, task) cells, with the largest gains on high-variance settings: a reliability gain from allocation alone. |
| title | Compute Allocation in Evolutionary Search: From Depth-Breadth to Multi-Armed Bandits |
| topic | Computation and Language Artificial Intelligence Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2605.29268 |