# Generating Long Semantic IDs in Parallel for Recommendation

arXiv: https://arxiv.org/abs/2506.05781 · HTML: https://mlsanity.com/paper/2506.05781/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2025.

## Built on by (most cited first, 30 of 30)

- [MMQ: Multimodal Mixture-of-Quantization Tokenization for Semantic ID Generation and User Behavioral Adaptation](https://mlsanity.com/paper/2508.15281/) (2025)
- [Masked Diffusion for Generative Recommendation](https://mlsanity.com/paper/2511.23021/) (2025)
- [Generative Recommendation for Large-Scale Advertising](https://mlsanity.com/paper/2602.22732/) (2026)
- [Understanding Generative Recommendation with Semantic IDs from a Model-scaling View](https://mlsanity.com/paper/2509.25522/) (2025)
- [NEZHA: A Zero-sacrifice and Hyperspeed Decoding Architecture for Generative Recommendations](https://mlsanity.com/paper/2511.18793/) (2025)
- [Stop Treating Collisions Equally: Qualification-Aware Semantic ID Learning for Recommendation at Industrial Scale](https://mlsanity.com/paper/2603.00632/) (2026)
- [GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models](https://mlsanity.com/paper/2507.06507/) (2025)
- [Pctx: Tokenizing Personalized Context for Generative Recommendation](https://mlsanity.com/paper/2510.21276/) (2025)
- [Rethinking Recommendation Paradigms: From Pipelines to Agentic Recommender Systems](https://mlsanity.com/paper/2603.26100/) (2026)
- [Understanding Semantic IDs: From Item Representation to Item Selection in Generative Recommendation](https://mlsanity.com/paper/2607.24995/) (2026)
- [From Past To Path: Masked History Learning for Next-Item Prediction in Generative Recommendation](https://mlsanity.com/paper/2509.23649/) (2025)
- [Masked Diffusion Generative Recommendation](https://mlsanity.com/paper/2601.19501/) (2026)
- [Rethinking Generative Recommender Tokenizer: Recsys-Native Encoding and Semantic Quantization Beyond LLMs](https://mlsanity.com/paper/2602.02338/) (2026)
- [LLMs Need Encoders for Semantic IDs Too](https://mlsanity.com/paper/2606.00324/) (2026)
- [UniGRec: Unified Generative Recommendation with Soft Identifiers for End-to-End Optimization](https://mlsanity.com/paper/2601.17438/) (2026)
- [GenRecEdit: Adapting Model Editing for Generative Recommendation with Cold-Start Items](https://mlsanity.com/paper/2603.14259/) (2026)
- [History-Conditioned Joint-Prefix Alignment for Generative Recommendation](https://mlsanity.com/paper/2608.29179/) (2026)
- [Recommendation Retrievers Need Verifiers: Universal Generative Reranking for Sequential Recommendations](https://mlsanity.com/paper/2609.12270/) (2026)
- [Xray-Visual Models: Scaling Vision models on Industry Scale Data](https://mlsanity.com/paper/2602.16918/) (2026)
- [SIGMA: A Semantic-Grounded Instruction-Driven Generative Multi-Task Recommender at AliExpress](https://mlsanity.com/paper/2602.22913/) (2026)
- [OCP: Orthogonal Constrained Projection for Sparse Scaling in Industrial Commodity Recommendation](https://mlsanity.com/paper/2603.18697/) (2026)
- [One Model, Two Markets: Bid-Aware Generative Recommendation](https://mlsanity.com/paper/2603.22231/) (2026)
- [AgenticRS-Architecture: System Design for Agentic Recommender Systems](https://mlsanity.com/paper/2603.26085/) (2026)
- [Deep Interest Mining for Intent-Enriched Semantic IDs in Multimodal Generative Recommendation](https://mlsanity.com/paper/2604.20861/) (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)
- [Adaptive Item-based Collaborative Structures via Noise Rescheduling in Diffusion for Generative Recommendation](https://mlsanity.com/paper/2608.23400/) (2026)
- [Tlow: Flow-based Item Tokenizer for Recommendation](https://mlsanity.com/paper/2608.24176/) (2026)
- [ReMem: Rethinking Perception and Memory in Long-Context Recommendation Agents](https://mlsanity.com/paper/2609.37311/) (2026)
- [Neither Black nor White: Balancing Semantic and Collaborative Signals with Graph-Informed Semantic IDs (GrIS)](https://mlsanity.com/paper/2610.01533/) (2026)
- [Personalized LLM Recommendation with Semantic IDs: Autoregressive Beam Search vs. Multi-Token Prediction](https://mlsanity.com/paper/2610.15864/) (2026)

## Abstract (arXiv)

Semantic ID-based recommendation models tokenize each item into a small number of discrete tokens that preserve specific semantics, leading to better performance, scalability, and memory efficiency. While recent models adopt a generative approach, they often suffer from inefficient inference due to the reliance on resource-intensive beam search and multiple forward passes through the neural sequence model. As a result, the length of semantic IDs is typically restricted (e.g. to just 4 tokens), limiting their expressiveness. To address these challenges, we propose RPG, a lightweight framework for semantic ID-based recommendation. The key idea is to produce unordered, long semantic IDs, allowing the model to predict all tokens in parallel. We train the model to predict each token independently using a multi-token prediction loss, directly integrating semantics into the learning objective. During inference, we construct a graph connecting similar semantic IDs and guide decoding to avoid generating invalid IDs. Experiments show that scaling up semantic ID length to 64 enables RPG to outperform generative baselines by an average of 12.6% on the NDCG@10, while also improving inference efficiency. Code is available at: https://github.com/facebookresearch/RPG_KDD2025.