Combee: Scaling Prompt Learning for Self-Improving Language Model Agents
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, curr...
| Authors | Hanchen Li, Runyuan He, Qizheng Zhang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 5 April 2026 |
| Last updated at source | 25 September 2026 |
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