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.

翻译：近期大语言模型（LLMs）的成功重新激发了关于推荐系统能否实现类似扩展效益的探讨。传统推荐系统以海量嵌入表为主导，随着嵌入维度增加往往趋于性能饱和。相比之下，新兴的生成范式采用自回归Transformer生成的紧凑语义ID（SID）序列替代嵌入表示。然而当前多数工业级部署仍属专有系统，导致两个根本性问题悬而未决：（1）预期扩展定律在公开基准上是否成立？（2）实现竞争性性能所需的最小后训练方案是什么？本文提出MiniOneRec——据我们所知首个完全开源的生成式推荐框架，提供覆盖SID构建、监督微调及面向推荐的强化学习的端到端工作流。我们通过残差量化变分自编码器生成SID，并在Amazon Review数据集上对0.5B至7B参数的Qwen骨干网络进行后训练。实验表明，随着模型规模扩大，训练损失与评估损失均呈现持续下降趋势，验证了生成式方法的参数效率。为进一步提升性能，我们提出轻量级高效后训练流程：（1）实施全流程SID对齐机制；（2）应用约束解码与混合奖励的强化学习。这些技术共同显著提升了排序准确率与候选多样性。