Improving LLM-Generated Process Model Quality Through Reinforcement Learning: The Role of Reward Function Design
arXiv:2607.06175v1 Announce Type: cross 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
The rapid advancement and widespread adoption of Large Language Models necessitate continuous improvements in their output quality, especially for critical enterprise applications like process modeling, pushing research into advanced optimization techniques like RL.
Improving the quality of LLM-generated process models through sophisticated reward function design can significantly enhance automation and accuracy in critical business operations, reducing manual intervention and error.
The ability to generate higher-quality, multi-dimensional process models from natural language implies a reduction in the gap between human intent and automated execution in enterprise software.
- · AI software developers
- · Enterprise automation platforms
- · Businesses adopting LLM-driven process automation
- · Manual process modelers
- · Companies slow to integrate LLM-driven automation
More accurate and reliable automated business process management becomes achievable.
Reduced operational costs and increased efficiency across various industries due to better process automation.
The proliferation of context-aware, autonomous AI agents capable of self-organizing and optimizing complex workflows.
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Read at arXiv cs.AI