🧠 GREAM: Generative Reasoning Recommendation Model

Paper: Generative Reasoning Recommendation via LLMs, 2025.
Authors: Minjie Hong*, Zetong Zhou*, Zirun Guo, Ziang Zhang, Ruofan Hu, Weinan Gan, Jieming Zhu, Zhou Zhao†
Repository: https://github.com/Indolent-Kawhi/GRRM
HF Papers Link: https://huggingface.co/papers/2510.20815

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🧩 Model Summary

GREAM (Generative Reasoning Recommendation Model) is a large language model (LLM)-based generative reasoning recommender designed to unify understanding, reasoning, and prediction for recommendation tasks.
It introduces a reasoning-enhanced, verifiable reinforcement learning framework that allows both high-throughput direct recommendations and interpretable reasoning-based outputs.

Key Features

• Collaborative–Semantic Alignment: Fuses textual (titles, descriptions, reviews) and behavioral signals to align linguistic and collaborative semantics.
• Reasoning Curriculum Activation: Builds synthetic Chain-of-Thought (CoT) data and trains via curriculum to develop causal reasoning for recommendations.
• Sparse-Regularized Group Policy Optimization (SRPO): Enables stable RL fine-tuning using Residual-Sensitive Verifiable Rewards and Bonus-Calibrated Group Advantage Estimation for sparse feedback.

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🧠 Model Architecture

Component	Description
Backbone	Qwen3-4B-Instruct
Indexing	Residual Quantization (RQ-KMeans, 5 levels, 256 values per level)
Training Phases	① Collaborative–Semantic Alignment → ② Reasoning Curriculum Activation → ③ SRPO Reinforcement Learning
Inference Modes	- Direct Sequence Recommendation: low-latency item generation - Sequential Reasoning Recommendation: interpretable CoT reasoning chains
RL Framework	Verl + SGLang backend

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📚 Training Data

Data Type	Source	Description
Dalign	Amazon Review Datasets (Beauty, Sports, Instruments)	Sequential, semantic reconstruction, and preference understanding tasks
Dreason	Synthetic CoT data generated via GPT-5 / Qwen3-30B / Llama-3.1	Multi-step reasoning sequences with <think>...</think> and <answer>...</answer> supervision
Text Sources	Item titles, descriptions, and high-quality reviews	Combined and rewritten to form dense item semantics

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📊 Evaluation

Datasets

• Amazon-Beauty
• Amazon-Sports & Outdoors
• Amazon-Musical Instruments

Citation

@misc{hong2025generativereasoningrecommendationllms,
      title={Generative Reasoning Recommendation via LLMs}, 
      author={Minjie Hong and Zetong Zhou and Zirun Guo and Ziang Zhang and Ruofan Hu and Weinan Gan and Jieming Zhu and Zhou Zhao},
      year={2025},
      eprint={2510.20815},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2510.20815}, 
}