[2604.04247] Combee: Scaling Prompt Learning for Self-Improving Language Model Agents

Computer Science > Artificial Intelligence arXiv:2604.04247 (cs) [Submitted on 5 Apr 2026] Title:Combee: Scaling Prompt Learning for Self-Improving Language Model Agents Authors:Hanchen Li, Runyuan He, Qizheng Zhang, Changxiu Ji, Qiuyang Mang, Xiaokun Chen, Lakshya A Agrawal, Wei-Liang Liao, Eric Yang, Alvin Cheung, James Zou, Kunle Olukotun, Ion Stoica, Joseph E. Gonzalez View a PDF of the paper titled Combee: Scaling Prompt Learning for Self-Improving Language Model Agents, by Hanchen Li and 13 other authors View PDF HTML (experimental) Abstract:Recent advances in prompt learning allow large language model agents to acquire task-relevant knowledge from inference-time context without parameter changes. For example, existing methods (like ACE or GEPA) can learn system prompts to improve accuracy based on previous agent runs. However, these methods primarily focus on single-agent or low-parallelism settings. This fundamentally limits their ability to efficiently learn from a large set of collected agentic traces. It would be efficient and beneficial to run prompt learning in parallel to accommodate the growing trend of learning from many agentic traces or parallel agent executions. Yet without a principled strategy for scaling, current methods suffer from quality degradation with high parallelism. To improve both the efficiency and quality of prompt learning, we propose Combee, a novel framework to scale parallel prompt learning for self-improving agents. Combee speeds up l...