$\text{R}^2\text{ec}$: Towards Large Recommender Models with Reasoning
Runyang You, Yongqi Li, Xinyu Lin, Xin Zhang, Wenjie Wang, Wenjie Li, Liqiang Nie
Recent advances in Large Recommender Models extend LLMs for recommendation tasks via encoding or item generation,while reasoning ability of LLM is typically utilized as an external module to produce extra inputs or features for conventional architectures, resulting in misaligned optimization and underutilized LLM capacity. We propose  $\text{R}^2\text{ec}$, a unified large recommender model that interleaves reasoning and recommendation in a single autoregressive pass. With a prompt encoding user history, the model generates a reasoning chain, whose hidden state is used to score items—seamlessly fusing generative reasoning and discriminative ranking.To train this architecture, we introduce a policy optimization framework that treats reasoning and item prediction as sequential actions in one trajectory, enabling joint optimization of reasoning and recommendation.Experiments on three public datasets show that  $\text{R}^2\text{ec}$ significantly outperforms existing LLM-based baselines, achieving up to 50\% gains in NDCG@5.