标题： MiniOneRec：一个用于生成推荐扩展的开源框架 显示英文标题

标题： MiniOneRec: An Open-Source Framework for Scaling Generative Recommendation

摘要： 大型语言模型（LLMs）的最新成功重新引发了人们对推荐系统是否能获得类似扩展效益的兴趣。传统的推荐系统主要由庞大的嵌入表主导，在嵌入维度增加时往往会达到瓶颈。相比之下，新兴的生成范式用自回归Transformer生成的紧凑语义ID（SID）序列取代了嵌入。然而，大多数工业部署仍然是专有的，留下了两个基本问题：（1）预期的扩展定律在公共基准上是否成立？（2）什么是最小的后训练方案能够实现有竞争力的性能？我们提出MiniOneRec，据我们所知，这是第一个完全开源的生成推荐框架，提供了一个端到端的工作流程，涵盖SID构建、监督微调和面向推荐的强化学习。我们通过残差量化VAE生成SID，并在Amazon Review数据集上对参数范围从0.5B到7B的Qwen主干进行后训练。我们的实验显示，随着模型规模的增加，训练和评估损失均呈现出一致的下降趋势，验证了生成方法的参数效率。为了进一步提高性能，我们提出了一种轻量但有效的后训练流程，（1）强制全过程SID对齐，（2）应用具有约束解码和混合奖励的强化学习。这些技术共同显著提升了排序准确性和候选多样性。 显示英文摘要

摘要： The recent success of large language models (LLMs) has renewed interest in whether recommender systems can achieve similar scaling benefits. Conventional recommenders, dominated by massive embedding tables, tend to plateau as embedding dimensions grow. In contrast, the emerging generative paradigm replaces embeddings with compact Semantic ID (SID) sequences produced by autoregressive Transformers. Yet most industrial deployments remain proprietary, leaving two fundamental questions open: (1) Do the expected scaling laws hold on public benchmarks? (2) What is the minimal post-training recipe that enables competitive performance? We present MiniOneRec, to the best of our knowledge, the first fully open-source generative recommendation framework, which provides an end-to-end workflow spanning SID construction, supervised fine-tuning, and recommendation-oriented reinforcement learning. We generate SIDs via a Residual Quantized VAE and post-train Qwen backbones ranging from 0.5B to 7B parameters on the Amazon Review dataset. Our experiments reveal a consistent downward trend in both training and evaluation losses with increasing model size, validating the parameter efficiency of the generative approach. To further enhance performance, we propose a lightweight yet effective post-training pipeline that (1) enforces full-process SID alignment and (2) applies reinforcement learning with constrained decoding and hybrid rewards. Together, these techniques yield significant improvements in both ranking accuracy and candidate diversity.