Adaptive Creativity Evaluation through Multi-turn Dialogue Driven by Reinforcement Learning

In recent years, with the rapid development of deep learning in code generation, text writing, and experimental design, accurately capturing and assessing researchers’ creativity has become a key issue in need of breakthrough. Traditional creativity evaluation methods, being static, subjective, and time-consuming, fail to reflect the dynamic iteration and multidimensional characteristics of creative thinking. To address this, we propose a dynamic creativity evaluation framework (DynaCREA) based on reinforcement learning, featuring an adaptive decision-making and feedback mechanism that utilizes real-time evaluation of user interaction and creativity metrics. Through multi-turn interactions between researchers and large language models, the framework integrates multimodal tasks, including textual contexts, verbal expression, and image-inspired tasks, to achieve real-time quantification of key dimensions of creativity (such as originality, fluency, elaboration, and flexibility). The intelligent agent leverages immediate feedback to adaptively adjust the design of subsequent tasks, thereby forming a novel creativity evaluation method that is both theoretically rigorous and practically efficient. Preliminary experimental results show that, after training, the intelligent agent meets a high degree of consistency with human evaluators across all indicators, demonstrating broad prospects for application in complex research environments.