Preprint
Aug 2026
Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging
This work proposes the first model merging framework for reasoning compression in recommender systems, and proposes selective injection of the concise behaviour of the fast-thinking model into the slow-thinking model and reducing reasoning verbosity without compromising recommendation quality.
Linh Le, Tong Chen, S. Sadiq et al.
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