arXiv:2605.27881v2 Announce Type: replace Abstract: Search agents powered by large language models can autonomously decompose queries, retrieve information, and synthesize answers through multi-step reasoning. However, the rapid growth of training methods has outpaced controlled comparison: existing works differ in retrieval corpora, reward designs, and training protocols, making it unclear what actually drives improvements. We present a controlled empirical study that isolates three under-explored dimensions of search agent training. First, we identify a critical data-coverage issue in the wi
The rapid advancement of large language models has led to an explosion of training methodologies, necessitating controlled studies to identify effective components for building advanced search agents.
Understanding the core drivers of search agent performance is crucial for the efficient and impactful development of autonomous AI systems across various applications.
This research provides a clearer understanding of which aspects (retrieval corpus, reward design, training protocols) are most critical for improving search agents, guiding future development efforts.
- · AI model developers
- · Search engine companies
- · Generative AI platforms
- · Researchers in AI agents
- · Companies with inefficient AI training pipelines
- · Projects based on suboptimal agent designs
Improved performance and efficiency of large language model-based search agents will accelerate AI capabilities.
More reliable and autonomous AI agents will emerge, reducing the need for human oversight in certain information retrieval and synthesis tasks.
The enhanced capability of AI agents could significantly disrupt traditional information industries and professional workflows.
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Read at arXiv cs.CL