title: R^2ec: Towards Large Recommender Models with Reasoning
summary: 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.
paper_authors._id: 683fb5d3a09aefea70733fa3
paper_authors.name: Runyang You
paper_authors.status: extracted_confirmed
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paper_authors.fullname: Runyang
paper_authors.type: user
paper_authors.user: dd101bb
paper_authors._id: 683fb5d3a09aefea70733fa4
paper_authors.name: Yongqi Li
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paper_authors.fullname: tensorslow
paper_authors.type: user
paper_authors.user: tensorslow
paper_authors._id: 683fb5d3a09aefea70733fa5
paper_authors.name: Xinyu Lin
paper_authors._id: 683fb5d3a09aefea70733fa6
paper_authors.name: Xin Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-04T08:55:59.107Z
paper_authors._id: 63b6dbc8ccebeadccc888456
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1673396893898-63b6dbc8ccebeadccc888456.jpeg
paper_authors.fullname: Xin Zhang
paper_authors.type: user
paper_authors.user: izhx
paper_authors._id: 683fb5d3a09aefea70733fa7
paper_authors.name: Wenjie Wang
paper_authors._id: 683fb5d3a09aefea70733fa8
paper_authors.name: Wenjie Li
paper_authors._id: 683fb5d3a09aefea70733fa9
paper_authors.name: Liqiang Nie
paper_title: R^2ec: Towards Large Recommender Models with Reasoning
paper_summary: 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.
paper_ai_summary: A unified large recommender model with intrinsic reasoning capabilities is proposed, facilitating interleaved reasoning and recommendation using a reinforcement learning framework called RecPO.
paper_ai_keywords: recommender models LLMs intrinsic reasoning autoregressive process reinforcement learning RecPO fused reward scheme Hit@5 NDCG@20