# OneRec Technical Report

arXiv: https://arxiv.org/abs/2506.13695 · HTML: https://mlsanity.com/paper/2506.13695/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2025.

## Built on by (most cited first, 25 of 25)

- [Kwai Keye-VL Technical Report](https://mlsanity.com/paper/2507.01949/) (2025)
- [Reasoning over Semantic IDs Enhances Generative Recommendation](https://mlsanity.com/paper/2603.23183/) (2026)
- [OneReason Technical Report](https://mlsanity.com/paper/2606.06260/) (2026)
- [S2GR: Stepwise Semantic-Guided Reasoning in Latent Space for Generative Recommendation](https://mlsanity.com/paper/2601.18664/) (2026)
- [Vectorizing the Trie: Efficient Constrained Decoding for LLM-based Generative Retrieval on Accelerators](https://mlsanity.com/paper/2602.22647/) (2026)
- [Generative Reasoning Recommendation via LLMs](https://mlsanity.com/paper/2510.20815/) (2025)
- [KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta](https://mlsanity.com/paper/2512.23236/) (2025)
- [DiffGRM: Diffusion-based Generative Recommendation Model](https://mlsanity.com/paper/2510.21805/) (2025)
- [xLLM Technical Report](https://mlsanity.com/paper/2510.14686/) (2025)
- [Rethinking Recommendation Paradigms: From Pipelines to Agentic Recommender Systems](https://mlsanity.com/paper/2603.26100/) (2026)
- [PROMISE: Process Reward Models Unlock Test-Time Scaling Laws in Generative Recommendations](https://mlsanity.com/paper/2601.04674/) (2026)
- [Rethinking Generative Recommender Tokenizer: Recsys-Native Encoding and Semantic Quantization Beyond LLMs](https://mlsanity.com/paper/2602.02338/) (2026)
- [QARM V2: Quantitative Alignment Multi-Modal Recommendation for Reasoning User Sequence Modeling](https://mlsanity.com/paper/2602.08559/) (2026)
- [Grounded Token Initialization for New Vocabulary in LMs for Generative Recommendation](https://mlsanity.com/paper/2604.02324/) (2026)
- [Difficulty-Aware Semantic-ID Optimization for Generative Recommendation](https://mlsanity.com/paper/2608.20611/) (2026)
- [Recommendation Retrievers Need Verifiers: Universal Generative Reranking for Sequential Recommendations](https://mlsanity.com/paper/2609.12270/) (2026)
- [MPFormer: Adaptive Framework for Industrial Multi-Task Personalized Sequential Retriever](https://mlsanity.com/paper/2508.20400/) (2025)
- [AgenticRS-Architecture: System Design for Agentic Recommender Systems](https://mlsanity.com/paper/2603.26085/) (2026)
- [Enhancing Local Life Service Recommendation with Agentic Reasoning in Large Language Model](https://mlsanity.com/paper/2604.14051/) (2026)
- [ReCast: Recasting Learning Signals for Reinforcement Learning in Generative Recommendation](https://mlsanity.com/paper/2604.22169/) (2026)
- [Intuition-Guided Latent Reasoning for LLM-Based Recommendation](https://mlsanity.com/paper/2606.27684/) (2026)
- [ShopX: A Foundation Model for Intent-to-Item Fulfillment in Agentic Shopping](https://mlsanity.com/paper/2606.31693/) (2026)
- [WhisperRec: Latent Reasoning for Efficient Foundation Recommendation Models](https://mlsanity.com/paper/2607.26621/) (2026)
- [Interpretable Representation via LLM-Driven Generative Disentanglement for Local-Life Service Recommendation](https://mlsanity.com/paper/2607.27944/) (2026)
- [Revisiting Generative vs. Dense Retrieval: A Static Value Prior Reverses the Gap](https://mlsanity.com/paper/2610.19065/) (2026)

## Abstract (arXiv)

Recommender systems have been widely used in various large-scale user-oriented platforms for many years. However, compared to the rapid developments in the AI community, recommendation systems have not achieved a breakthrough in recent years. For instance, they still rely on a multi-stage cascaded architecture rather than an end-to-end approach, leading to computational fragmentation and optimization inconsistencies, and hindering the effective application of key breakthrough technologies from the AI community in recommendation scenarios. To address these issues, we propose OneRec, which reshapes the recommendation system through an end-to-end generative approach and achieves promising results. Firstly, we have enhanced the computational FLOPs of the current recommendation model by 10 × and have identified the scaling laws for recommendations within certain boundaries. Secondly, reinforcement learning techniques, previously difficult to apply for optimizing recommendations, show significant potential in this framework. Lastly, through infrastructure optimizations, we have achieved 23.7% and 28.8% Model FLOPs Utilization (MFU) on flagship GPUs during training and inference, respectively, aligning closely with the LLM community. This architecture significantly reduces communication and storage overhead, resulting in operating expense that is only 10.6% of traditional recommendation pipelines. Deployed in Kuaishou/Kuaishou Lite APP, it handles 25% of total queries per second, enhancing overall App Stay Time by 0.54% and 1.24%, respectively. Additionally, we have observed significant increases in metrics such as 7-day Lifetime, which is a crucial indicator of recommendation experience. We also provide practical lessons and insights derived from developing, optimizing, and maintaining a production-scale recommendation system with significant real-world impact.