arXiv:2506.00400v4 Announce Type: replace-cross Abstract: LLM-based prompt optimization, which uses LLM-provided ``textual gradients'' (feedback) to refine prompts, has emerged as an effective method for automatic prompt engineering. However, its scalability and stability are unclear when using more data in training. We systematically investigate the potential and challenges of scaling training data in textual gradient descent. We show that naively scaling training examples is infeasible due to both explicit context-length limits and an implicit context wall, where long-context degradation yie
The increasing reliance on LLMs for prompt optimization is encountering scalability limits, necessitating new techniques to leverage larger datasets efficiently.
Improving the scalability and stability of textual gradient descent can significantly enhance the efficiency and performance of LLM-based autonomous systems and prompt engineering.
The ability to effectively integrate more training data into textual gradient methods changes the potential ceiling for LLM optimization and agentic system development.
- · AI model developers
- · prompt engineering platforms
- · LLM-based autonomous agents
- · large AI labs
- · inefficient prompt optimization methods
- · companies reliant on limited context windows
More robust and effective LLM-based prompt optimization becomes possible by addressing current scalability and stability issues.
This innovation could accelerate the development and deployment of sophisticated AI agents that rely heavily on optimized prompt workflows.
Enhanced prompt engineering capabilities might democratize advanced AI usage, potentially reducing reliance on expert human intervention for complex AI tasks.
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Read at arXiv cs.AI