Yitong Cui, Liu Liu, Baosheng Yu et al.
Large language models (LLMs) have exhibited significant capabilities in addressing challenging problems throughout various fields, often through the use of agentic workflows that adhere to structured instructions and multi-step procedures. However, designing such workflows demands substantial manual effort, posing challenges to scalability and generalizability. Recent studies have aimed to minimize the human intervention needed for their construction, leading to advances in automated techniques for optimizing agentic workflows. However, current approaches are often constrained by their limited representational capacity, insufficient adaptability, weak scalability, and pairwise comparison paradigm -- issues that stem primarily from a dependence on discrete optimization techniques. To overcome these limitations, we introduce a new score-based preference approach, refereed as SPOGW, which operates directly on cardinal reward signals through group-wise comparison and enables more efficient and stable optimization in a continuous space. SPOGW incorporates Iterative offline GRPO (ioGRPO) with advantage-masked KL divergence (mKL), which regulates training update by placing greater emphasis on the advantageous regions of the policy response. In five benchmark datasets covering mathematical reasoning, coding, and question answering, SPOGW matches or exceeds the performance of current state-of-the-art approaches, presenting a viable and forward-looking methodology for automated generation and optimization of agentic workflows.