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Kuai-RL

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# Kuai-RL

A list of resources about Reinforcement Learning works in Kuaishou Technology.
1. ["Two-Stage Constrained Actor-Critic for Short Video Recommendation."](https://arxiv.org/pdf/2302.01680.pdf) 
WWW 2023.
\[[code](https://github.com/AIDefender/TSCAC)\] 
Keywords: Constrained Reinforcement Learning

2. ["Reinforcing User Retention in a Billion Scale Short Video Recommender System."](https://arxiv.org/pdf/2302.01724.pdf) 
WWW 2023 Industry Track. 
Keywords: RL for Retention Optimization

3. ["Exploration and Regularization of the Latent Action Space in Recommendation."](https://arxiv.org/pdf/2302.03431.pdf) 
WWW 2023. 
\[[code](https://github.com/CharlieMat/Hyper-Actor-Critic-for-Recommendation)\] 
Keywords: Large Action Space

4. ["ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actor."](https://arxiv.org/pdf/2206.02620.pdf) 
ICLR 2023.
\[[code](https://www.dropbox.com/sh/btf0drgm99vmpfe/AADtkmOLZPQ0sTqmsA0f0APna?dl=0)\] 
Keywords: Offline Reinforcement Learning for Rec

5. ["PrefRec: Recommender Systems with Human Preferences for Reinforcing Long-term User Engagement."](https://personal.ntu.edu.sg/boan/papers/KDD23_PrefRec.pdf) 
KDD 2023.
\[[code](https://www.dropbox.com/sh/hgsqg5fabnvmp26/AABF-2dvarI_bdyygYEt5aw7a?dl=0)\] 
Keywords: Learning from Preferences

6. ["Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation."](https://arxiv.org/pdf/2307.04571.pdf) 
SIGIR 2023 
\[[code](https://github.com/chongminggao/DORL-codes)\] 
Keywords: Offline RL, Matthew effect

7. ["KuaiSim: A Comprehensive Simulator for Recommender Systems"](https://arxiv.org/pdf/2309.12645.pdf) 
NeurIPS 2023 Datasets and Benchmarks track. \[[code](https://github.com/CharlieMat/KRLBenchmark)\] 
Keywords: Comprehensive Simulator for RL-based Rec

8. ["UNEX-RL: Reinforcing Long-Term Rewards in Multi-Stage Recommender Systems with UNidirectional EXecution."](https://arxiv.org/abs/2401.06470) 
AAAI 2024. 
Keywords: Multi-agent Reinforcement Learning

9. ["Future Impact Decomposition in Request-level Recommendations."](https://arxiv.org/abs/2401.16108)
KDD 2024. 
Keywords: Reinforcement Learning for Request-level Recommendations

10. ["Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention."](https://arxiv.org/pdf/2404.03637)
SIGIR 2024. 
Keywords: Decision Transformer for Rec

11. ["LLM-Powered Efficient User Simulator for Recommender System."](https://arxiv.org/pdf/2412.16984)
AAAI 2025. 
Keywords: LLM-based Simulator for RL-based Rec

12. ["AURO: Reinforcement Learning for Adaptive User Retention Optimization in Recommender Systems."]()
WWW 2025. 
Keywords: RL for Adaptive Environment

13. ["GAS: Generative Auto-bidding with Post-training Search."](https://arxiv.org/pdf/2412.17018)
WWW 2025. \[[code](https://github.com/yewen99/GAS_WWW-25/tree/main)\]
Keywords: G4RL, Test-time Search

14. ["Generative Auto-Bidding with Value-Guided Explorations."](https://arxiv.org/pdf/2412.17018)
SIGIR 2025. \[[code](https://github.com/Applied-Machine-Learning-Lab/GAVE)\]
Keywords: G4RL, Offline Guided Explorations

## Contact

Please let us know through the following contacts if you have any comment:

Email: caiqingpeng@kuaishou.com

Kuai-RL

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