Learning From Each Other: Exploring A Novel Mutual Learning Paradigm for Enhancing Code Generation Performance

Le résumé fourni par la source

Large language model progress drives code generation research. Most works enhance performance via problem decomposition and knowledge distillation. However, they predominantly adopt a unidirectional approach to knowledge learning. Inspired by the mutually beneficial learning patterns observed in humans, this paper explores a mutual learning paradigm. Specifically, we first employ a dual-training architecture involving two lightweight models that learn the knowledge from each other for better performance. Then, we maximize mutual learning potential by exploring mutual self-correction and preference optimization to address knowledge sharing limits. The self-correction strategy incorporates error information generated by both the model itself and its peer, fostering a mutual refinement process. The preference optimization utilizes the excellent knowledge that has not been learned from the peer model to guide the learning. We conduct and analyze the proposed method on two public datasets, expanding the corpus for optimization. Experimental results show the effectiveness of our method, yielding compelling outcomes that underscore the potential of this collaborative learning approach.

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