Turning Database Grading into RL Rewards: Post-Training Agents with ThinkingBox
The article discusses the development of ThinkingBox, a tool that evaluates an AI agent's work by comparing the final database state with the expected outcome, and uses this as a reward function for training. The authors claim that this approach improved the performance of the Qwen3.8-27B model, increasing average success from 51.70% to 60.78% and the number of tasks completed correctly on all twenty attempts from 38 to 107.
The improvement was particularly notable in a specific hotel-booking task, where the RL fine-tuned model preserved the booking and payment state in all 20 attempts, whereas the base model failed to do so in 15 attempts.
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