Improving LLM-Generated Process Model Quality Through Reinforcement Learning: The Role of Reward Function Design

2026-07-07

AI Summary

This work addresses the limitations of large language models in generating high-quality BPMN process models, which are constrained by supervised fine-tuning data and the absence of well-defined multidimensional reward functions. The authors propose a reinforcement learning–based optimization approach that systematically explores a reward function encompassing 38 syntactic, pragmatic, and semantic metrics. They train Llama-3.1-8B and Qwen2.5-14B models across 48 configurations and find that uniformly weighted rewards outperform targeted weighting schemes, with significant interaction effects observed between reward composition and model architecture. Leveraging Group Relative Policy Optimization and an automated evaluation framework, the method substantially improves pragmatic and syntactic quality while preserving semantic fidelity and reducing output variability by over sixfold. All code is publicly released.

Abstract

Large language models (LLMs) can generate BPMN process models from natural-language descriptions, yet supervised fine-tuning (SFT) limits their output quality to the patterns present in the training data. Reinforcement learning (RL) can optimize beyond this ceiling using external quality measures, but how the reward function should be designed when quality is multi-dimensional remains unexplored. We present a systematic investigation of reward function design for RL-based process model generation, training two LLM families (Llama~3.1 8B, Qwen~2.5 14B) under 48 configurations using Group Sequence Policy Optimization with rewards derived from an automated evaluation framework comprising 38 metrics across syntactic, pragmatic, and semantic quality. Three findings emerge. First, RL significantly improves pragmatic and syntactic quality while preserving semantic fidelity, reducing output variability by more than sixfold. Second, equal reward weighting consistently outperforms targeted weighting: emphasizing a specific dimension fails to improve it and can collapse the model into a low-quality mode. Third, design choices interact with model architecture in non-trivial ways: the invalidity penalty is essential for one model but irrelevant for the other, and SFT initialization is indispensable for one architecture but counterproductive for another. These results demonstrate that reward composition is a primary determinant of optimization outcomes, with effects as large as the decision to apply RL itself. The findings generalize to any structured generation task where quality is assessed along multiple automated dimensions. We release our implementation and experimental code at https://github.com/chlauer99/RL_for_process_modeling.

Problem

• reward function design
• process model generation
• reinforcement learning
• multi-dimensional quality
• LLM

Innovation

• reward function design
• reinforcement learning
• process model generation
• multi-dimensional quality
• LLM alignment