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PlannerRFT
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data/publications.tsx

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@@ -148,6 +148,26 @@ export const publications: {
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time: '',
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timeline:[],
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},
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{
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title: "PlannerRFT: Reinforcing Diffusion Planners through Closed-Loop and Sample-Efficient Fine-Tuning",
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link: "https://arxiv.org/pdf/2601.12901",
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image: "/assets/publication/PlannerRFT.jpeg",
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author: "Hongchen Li, Tianyu Li, Jiazhi Yang, Haochen Tian, Caojun Wang, Lei Shi, Mingyang Shang, Zengrong Lin, Gaoqiang Wu, Zhihui Hao, Xianpeng Lang, Jia Hu, Hongyang Li",
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note: "arXiv 2026",
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noteoption: '',
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star: "",
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starlink: "",
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icon: [
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{
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type: "page",
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link: "/PlannerRFT/",
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},
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],
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description: `A closed-loop and sample-efficient RFT framework for diffusion-based planners.`,
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keys: ['end_to_end_ad'],
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time: '2026.1.19',
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timeline:['te2e', 'highlight'],
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},
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{
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title: "SimScale: Learning to Drive via Real-World Simulation at Scale",
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link: "https://arxiv.org/abs/2511.23369",
@@ -172,26 +192,6 @@ export const publications: {
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time: '2025.11.28',
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timeline:['te2e'],
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},
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{
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title: "PlannerRFT: Reinforcing Diffusion Planners through Closed-Loop and Sample-Efficient Fine-Tuning",
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link: "https://arxiv.org/pdf/2601.12901",
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image: "/assets/publication/PlannerRFT.jpeg",
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author: "Hongchen Li, Tianyu Li, Jiazhi Yang, Haochen Tian, Caojun Wang, Lei Shi, Mingyang Shang, Zengrong Lin, Gaoqiang Wu, Zhihui Hao, Xianpeng Lang, Jia Hu, Hongyang Li",
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note: "arXiv 2026",
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noteoption: '',
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star: "",
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starlink: "",
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icon: [
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{
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type: "page",
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link: "/PlannerRFT/",
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},
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],
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description: `A closed-loop and sample-efficient RFT framework for diffusion-based planners.`,
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keys: ['end_to_end_ad'],
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time: '2026.1.19',
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timeline:['te2e'],
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},
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{
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title: "Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving",
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link: "https://ieeexplore.ieee.org/document/11353028",

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