A unified large recommender model with intrinsic reasoning capabilities is proposed, facilitating interleaved reasoning and recommendation using a reinforcement learning framework called RecPO.
Large recommender models have extended LLMs as powerful recommenders via
encoding or item generation, and recent breakthroughs in LLM reasoning
synchronously motivate the exploration of reasoning in recommendation. Current
studies usually position LLMs as external reasoning modules to yield auxiliary
thought for augmenting conventional recommendation pipelines. However, such
decoupled designs are limited in significant resource cost and suboptimal joint
optimization. To address these issues, we propose \name, a unified large
recommender model with intrinsic reasoning capabilities. Initially, we
reconceptualize the model architecture to facilitate interleaved reasoning and
recommendation in the autoregressive process. Subsequently, we propose RecPO, a
corresponding reinforcement learning framework that optimizes \name\ both the
reasoning and recommendation capabilities simultaneously in a single policy
update; RecPO introduces a fused reward scheme that solely leverages
recommendation labels to simulate the reasoning capability, eliminating
dependency on specialized reasoning annotations. Experiments on three datasets
with various baselines verify the effectiveness of \name, showing relative
improvements of 68.67\% in Hit@5 and 45.21\% in NDCG@20. Code available at
https://github.com/YRYangang/RRec.
