Divide, Optimize, Merge: Scalable Fine-Grained Generative Optimization for LLM Agents (2025.findings-emnlp)
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Jiale Liu, Yifan Zeng, Shaokun Zhang, Chi Zhang, Malte Højmark-Bertelsen, Marie Normann Gadeberg, Huazheng Wang, Qingyun Wu
| Challenge: | LLM-based generative optimization has shown remarkable potential in improving agentic systems, but the current approach of prompting with the trajectories on the whole training dataset becomes untenable as datasets grow. |
| Approach: | They propose a scalable framework that divides large optimization tasks into manageable subsets and performs targeted optimizations. |
| Outcome: | The proposed framework outperforms conventional approach by 1.6-8.6% while reducing average prompt token consumption by 56.3%. |
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