# REG4Rec: Reasoning-Enhanced Generative Model for Large-Scale Recommendation Systems

arXiv: https://arxiv.org/abs/2508.15308 · HTML: https://mlsanity.com/paper/2508.15308/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Placed in LLM reasoning for recommendation, one of 17 directions in LLM reasoning.
- First posted on arXiv 21 Aug 2025.

## Placed in

- LLM reasoning / [LLM reasoning for recommendation](https://mlsanity.com/lr/llm-reasoning-for-recommendation/)

## Built on by (most cited first, 18 of 18)

- [RecoWorld: Building Simulated Environments for Agentic Recommender Systems](https://mlsanity.com/paper/2509.10397/) (2025)
- [OneReason Technical Report](https://mlsanity.com/paper/2606.06260/) (2026)
- [Rethinking Recommendation Paradigms: From Pipelines to Agentic Recommender Systems](https://mlsanity.com/paper/2603.26100/) (2026)
- [Masked Diffusion Generative Recommendation](https://mlsanity.com/paper/2601.19501/) (2026)
- [Spend Search Where It Pays: Value-Guided Structured Sampling and Optimization for Generative Recommendation](https://mlsanity.com/paper/2602.10699/) (2026)
- [Verifiable Reasoning for LLM-based Generative Recommendation](https://mlsanity.com/paper/2603.07725/) (2026)
- [HoloRec: Holistic Encoding and Interleaved Reasoning for Generative Recommendation](https://mlsanity.com/paper/2606.15331/) (2026)
- [LocalSUG: City-Preference-Enhanced LLM for Query Suggestion in Local-Life Services](https://mlsanity.com/paper/2603.04946/) (2026)
- [Beyond Semantic Relevance: Counterfactual Risk Minimization for Robust Retrieval-Augmented Generation](https://mlsanity.com/paper/2605.01302/) (2026)
- [EvoRec: Self Evolving Agentic Recommender Systems](https://mlsanity.com/paper/2606.28368/) (2026)
- [ExDR: Explanation-driven Dynamic Retrieval Enhancement for Multimodal Fake News Detection](https://mlsanity.com/paper/2601.15820/) (2026)
- [AIGQ: An End-to-End Hybrid Generative Architecture for E-commerce Query Recommendation](https://mlsanity.com/paper/2603.19710/) (2026)
- [AgenticRS-Architecture: System Design for Agentic Recommender Systems](https://mlsanity.com/paper/2603.26085/) (2026)
- [Chain of Evidence: Pixel-Level Visual Attribution for Iterative Retrieval-Augmented Generation](https://mlsanity.com/paper/2605.01284/) (2026)
- [WhisperRec: Latent Reasoning for Efficient Foundation Recommendation Models](https://mlsanity.com/paper/2607.26621/) (2026)
- [From a Static Multi-Level Small Semantic Codebook to a Dynamic Single-Level Large Semantic Codebook for Generative Recommendation](https://mlsanity.com/paper/2608.21012/) (2026)
- [Guiding the coarse levels of semantic IDs makes the fine levels learnable](https://mlsanity.com/paper/2609.22227/) (2026)
- [Cross-Country Code-Mixing for Generative Recommendation](https://mlsanity.com/paper/2609.28972/) (2026)

## Abstract (arXiv)

Sequential recommendation aims to predict a user's next action in large-scale recommender systems. While traditional methods often suffer from insufficient information interaction, recent generative recommendation models partially address this issue by directly generating item predictions. To better capture user intents, recent studies have introduced a reasoning process into generative recommendation, significantly improving recommendation performance. However, these approaches are constrained by the singularity of item semantic representations, facing challenges such as limited diversity in reasoning pathways and insufficient reliability in the reasoning process. To tackle these issues, we introduce REG4Rec, a reasoning-enhanced generative model that constructs multiple dynamic semantic reasoning paths alongside a self-reflection process, ensuring high-confidence recommendations. Specifically, REG4Rec utilizes an MoE-based parallel quantization codebook (MPQ) to generate multiple unordered semantic tokens for each item, thereby constructing a larger-scale diverse reasoning space. Furthermore, to enhance the reliability of reasoning, we propose a training reasoning enhancement stage, which includes Preference Alignment for Reasoning (PARS) and a Multi-Step Reward Augmentation (MSRA) strategy. PARS uses reward functions tailored for recommendation to enhance reasoning and reflection, while MSRA introduces future multi-step actions to improve overall generalization. During inference, Consistency-Oriented Self-Reflection for Pruning (CORP) is proposed to discard inconsistent reasoning paths, preventing the propagation of erroneous reasoning. Lastly, we develop an efficient offline training strategy for large-scale recommendation. Experiments on real-world datasets and online evaluations show that REG4Rec delivers outstanding performance and substantial practical value.