用强化学习训练模型联想能力，提升创作与编程表现
Training Emergent Joint Associations: A Reinforcement Learning Approach to Creative Thinking in Language Models
- 用提示词评估机制奖励高概念连接性输出
- 故事更原创连贯，编程和数据可视化更灵活抽象
- 适合想提升AI创造性的研究者与开发者
联想思维——将看似无关的想法联系起来的能力——是人类创造力和问题解决的基础。本文探讨了基于联想思维原则的强化学习（RL）是否能提升模型在多样生成任务中的表现，包括故事写作、代码生成和图表创建。我们提出一种基于提示的评估机制的强化学习框架，引入创意研究中已有的发散思维指标。通过该框架微调基础语言模型，以奖励表现出更高新颖性（即更高概念连通性）的输出。实验结果表明，基于强化学习训练的联想思维模型不仅生成更原创且连贯的故事，还在编程和数据可视化等任务中展现出更强的抽象能力和灵活性。研究为通过强化学习建模认知创造力原理，从而实现更具适应性和生成性的智能系统提供了初步证据。
原文摘要 · Abstract (English)
Associative thinking--the ability to connect seemingly unrelated ideas--is a foundational element of human creativity and problem-solving. This paper explores whether reinforcement learning (RL) guided by associative thinking principles can enhance a model's performance across diverse generative tasks, including story writing, code generation, and chart creation. We introduce a reinforcement learning framework that uses a prompt-based evaluation mechanism, incorporating established divergent thinking metrics from creativity research. A base language model is fine-tuned using this framework to reward outputs demonstrating higher novelty through higher degrees of conceptual connectivity. Interestingly, the experimental results suggest that RL-based associative thinking-trained models not only generate more original and coherent stories but also exhibit improved abstraction and flexibility in tasks such as programming and data visualization. Our findings provide initial evidence that modeling cognitive creativity principles through reinforcement learning can yield more adaptive and generative AI.
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