OpenOneRec

<div align="center"> <h1>OpenOneRec</h1> <p align="center"> <strong>An Open Foundation Model and Benchmark to Accelerate Generative Recommendation</strong> </p> <p align="center"> <a href="https://huggingface.co/OpenOneRec"> <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-OneRec-ffc107?color=ffc107&logoColor=white" /> </a> <a href="https://github.com/Kuaishou-OneRec/OpenOneRec"> <img alt="GitHub Code" src="https://img.shields.io/badge/GitHub-OpenOneRec-black?logo=github" /> </a> <a href="https://arxiv.org/abs/2512.24762"> <img alt="Paper" src="https://img.shields.io/badge/Paper-ArXiv-b31b1b?logo=arxiv" /> </a> <a href="#license"> <img alt="License" src="https://img.shields.io/badge/License-Apache%202.0-green" /> </a> </p> </div> <br>

📖 Introduction

OpenOneRec is an open-source framework designed to bridge the gap between traditional recommendation systems and Large Language Models (LLMs). While Generative Recommendation has shown promise, existing models often struggle with isolated data silos and a lack of reasoning capabilities.

To address this, we introduce a unified framework that comprises:

• RecIF-Bench: The first holistic Recommendation Instruction-Following Benchmark, containing 100M interactions from 200k users across heterogeneous domains (Short Video, Ads, Product).
• OneRec-Foundation Models: A family of models (1.7B & 8B) built on the Qwen3 backbone. The series includes Standard versions trained on our open-source dataset and Pro versions enhanced with a hundred-billion-token industrial corpus from Kuaishou.
• Full-Stack Pipeline: We open-source our comprehensive training pipeline, including data processing, co-pretraining, and post-training, to ensure full reproducibility and facilitate scaling law research in recommendation.

🔥 News

• [2026.1.1] 📑 The technical report has been released.
• [2026.1.1] 🎉 OneRec-Foundation models (1.7B, 8B) are now available on Hugging Face!
• [2026.1.1] 🚀 RecIF-Bench dataset and evaluation scripts are open-sourced.

📊 RecIF-Bench

We propose RecIF-Bench to rigorously assess the synergy between instruction following and domain-specific recommendation. It organizes 8 distinct tasks into a four-layer capability hierarchy:

• Layer 0: Semantic Alignment (Item Understanding)
• Layer 1: Fundamental Prediction (Short Video Rec, Ad Rec, Product Rec, Label Prediction)
• Layer 2: Instruction Following (Interactive Rec, Label-Conditional Rec)
• Layer 3: Reasoning (Recommendation Explanation)

The benchmark aggregates data from three domains: Short Video (Content), Ads (Commercial), and Product (E-commerce).

🤖 Model Zoo

The OpenOneRec-Foundation series is built upon the Qwen architecture, enhanced with Itemic Tokens for modality alignment and trained via a multi-stage protocol.

| Model | Backbone | Parameters | Description | Link | | :--- | :--- | :--- | :--- | :--- | | OneRec-1.7B | Qwen3-1.7B | 1.7B | Standard version trained on open-source data (~33B tokens) | HuggingFace | | OneRec-8B | Qwen3-8B | 8B | Standard version trained on open-source data (~33B tokens) | HuggingFace | | OneRec-1.7B-Pro | Qwen3-1.7B | 1.7B | Scaled-up version with expanded datasets (~130B tokens) | HuggingFace | | OneRec-8B-Pro | Qwen3-8B | 8B | Scaled-up version with expanded datasets (~130B tokens) | HuggingFace |

🏗️ Method & Architecture

OpenOneRec reframes recommendation as a general-purpose sequence modeling paradigm.

1. Items as Tokens

To bridge the modality gap, we treat items as a distinct modality using Itemic Tokens derived from hierarchical vector quantization. This allows the LLM to process interaction history as a cohesive context sequence.

2. Training Pipeline

Our framework utilizes the following recipe:

• Pre-Training: Integrates collaborative signals via Itemic-Text Alignment and Full-Parameter Co-Pretraining.
• Post-Training:
  • Stage 1: Multi-task Supervised Fine-tuning for basic instruction following.
  • Stage 2: On-policy Distillation to restore general reasoning performance.
  • Stage 3: Reinforcement Learning to enhance recommendation capabilities.

<div align="center"> <img src="assets/main_framework.png" width="80%" alt="OpenOneRec Overall Framework" /> <br> <em>Figure: The Overall Framework of OpenOneRec.</em> </div>

📈 Performance

Results on RecIF-Bench

OpenOneRec-Foundation achieves State-of-the-Art (SOTA) results across RecIF-Bench tasks, significantly outperforming baselines like LC-Rec and TIGER.

| Task | Metric | SASRec | TIGER | LC-Rec | OneRec-1.7B | OneRec-8B | OneRec-1.7B-Pro | OneRec-8B-Pro | | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | | Short Video Rec | Recall@32 | 0.0119 | 0.0132 | 0.0180 | 0.0272 | 0.0355 | 0.0274 | 0.0369 | | Ad Rec | Recall@32 | 0.0293 | 0.0581 | 0.0723 | 0.0707 | 0.0877 | 0.0735 | 0.0964 | | Product Rec | Recall@32 | 0.0175 | 0.0283 | 0.0416 | 0.0360 | 0.0470 | 0.0405 | 0.0538 | | Label-Cond. Rec | Recall@32 | 0.0140 | 0.0123 | 0.0170 | 0.0184 | 0.0228 | 0.0182 | 0.0235 | | Label Pred. | AUC | 0.6244 | 0.6675 | 0.6139 | 0.6184 | 0.6615 | 0.6071 | 0.6912 | | Interactive Rec | Recall@32 | -- | -- | 0.2394 | 0.1941 | 0.3032 | 0.2024 | 0.3458 | | Item Und. | LLM Score | -- | -- | 0.2517 | 0.3175 | 0.3202 | 0.3133 | 0.3209 | | Rec. Explanation | LLM Score | -- | -- | 3.9350 | 3.3540 | 3.6774 | 3.5060 | 4.0381 |

<div align="center"> <img src="assets/benchmark.png" width="80%" alt="Holistic Performance Overview of OpenOneRec." /> <br> <em>Holistic Performance Overview of OpenOneRec.</em> </div>

Cross-Domain Transferability

On the Amazon Benchmark (10 datasets), OpenOneRec demonstrates exceptional zero-shot/few-shot transfer capabilities, achieving an average 26.8% improvement in Recall@10 over the second-best method.

| Domain | SASRec | TIGER | LC-Rec | Ours | | :--- | :--- | :--- | :--- | :--- | | Baby | 0.0381 | 0.0318 | 0.0344 | 0.0513 | | Beauty | 0.0639 | 0.0628 | 0.0764 | 0.0924 | | Cell Phones | 0.0782 | 0.0786 | 0.0883 | 0.1036 | | Grocery | 0.0789 | 0.0691 | 0.0790 | 0.1029 | | Health | 0.0506 | 0.0534 | 0.0616 | 0.0768 | | Home | 0.0212 | 0.0216 | 0.0293 | 0.0390 | | Pet Supplies | 0.0607 | 0.0542 | 0.0612 | 0.0834 | | Sports | 0.0389 | 0.0331 | 0.0418 | 0.0547 | | Tools | 0.0437 | 0.0344 | 0.0438 | 0.0593 | | Toys | 0.0658 | 0.0527 | 0.0549 | 0.0953 |

Metric: Recall@10. Ours refers to OneRec-Foundation with text-augmented itemic tokens strategy. For implementation details, please refer to GRLM.

🚀 Quick Start

Code release and detailed usage instructions are coming soon.

Currently, you can load our models using transformers>=4.51.0:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "OpenOneRec/OneRec-8B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input
# case - prompt with itemic tokens
prompt = "这是一个视频：<|sid_begin|><s_a_340><s_b_6566><s_c_5603><|sid_end|>，帮我总结一下这个视频讲述了什么内容"
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
# Note: In our experience, default decoding settings may be unstable for small models.
# For 1.7B, we suggest: top_p=0.95, top_k=20, temperature=0.75 (during 0.6 to 0.8)
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

# parsing thinking content
try:
    # rindex finding 151668 (</think>)
    index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
    index = 0

thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")

print("thinking content:", thinking_content)
print("content:", content)

🛣️ Roadmap / Under Development

We are actively working on the following features:

• [ ] General-domain data: scripts to fetch and preprocess public general-domain corpora used in data/general_text.
• [ ] Reproducible environments: training pipeline Docker/Apptainer images for easier end-to-end reproduction.
• [ ] One-click reproduction: further code cleanup and streamlined training recipes for an end-to-end “run from scratch” experience.
• [ ] Docs & tutorials: improved documentation, tutorials, and best-practice guides.
• [ ] Unified VeRL integration: consolidate RL and distillation codepaths into a single, consistent VeRL-based implementation.
• [ ] More model sizes: support additional pretraining scales and configurations beyond current checkpoints.

Contributions are welcome! Please refer to the detailed documentation in each module.

📜 Citation

If you find our work helpful, please cite our technical report:

@misc{OpenOneRec,
title={OpenOneRec Technical Report}, 
      author={Guorui Zhou and Honghui Bao and Jiaming Huang and Jiaxin Deng and Jinghao Zhang and Junda She and Kuo Cai and Lejian Ren and Lu Ren and Qiang Luo and Q