FoodGPT: Reinforcement Post-Training of Large Language Models in the Food Delivery Domain

On-demand Food Delivery (OFD) platforms, such as Ele.me and Meituan, have transformed daily life by offering convenient ordering services. However, challenges remain in understanding user intentions and processing product-related text information. Existing NLP models, while advanced in general tasks, are less effective for OFD-specific needs due to data scarcity and high computational costs. This paper introduces FoodInstruct, a Chinese dataset with 1.6 million examples across 12 OFD-related NLP tasks, and FoodGPT, a domain-specific large language model. We propose an efficient reinforcement post-training framework that combines Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Proximal Policy Optimization (PPO) with an additional rule-based reward signal. The resulting foundation model, FoodGPT, enhances model performance while minimizing resource consumption. Experimental results demonstrate that FoodGPT outperforms general models, such as Qwen 2.5, Llama3.1 and DeepSeek-LLM, on OFD tasks, using fewer data and training iterations. The model has already been deployed across numerous applications within the company. For instance, it achieved a 0.57% increase in click-through rate and a 0.32% increase in user visit-to-purchase rate in the ITG online experiment. The core contributions are now publicly accessible at https://huggingface.co/elemenlp.