{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T14:56:09Z","timestamp":1781535369408,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":28,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/legalcode"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,6,16]]},"DOI":"10.1145\/3805622.3810780","type":"proceedings-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T14:42:57Z","timestamp":1781534577000},"page":"1721-1729","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Efficient Rationale-based Retrieval: On-policy Distillation from Generative Rerankers based on JEPA"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3604-5313","authenticated-orcid":false,"given":"Teng","family":"Chen","sequence":"first","affiliation":[{"name":"Geely AI Lab, Ningbo, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3352-8882","authenticated-orcid":false,"given":"Sheng","family":"Xu","sequence":"additional","affiliation":[{"name":"Geely AI Lab, Ningbo, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3132-440X","authenticated-orcid":false,"given":"Feixiang","family":"Guo","sequence":"additional","affiliation":[{"name":"Geely AI Lab, Ningbo, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8617-0616","authenticated-orcid":false,"given":"Xiaoyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Geely AI Lab, Ningbo, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6872-8910","authenticated-orcid":false,"given":"Qingqing","family":"Gu","sequence":"additional","affiliation":[{"name":"Geely AI Lab, Ningbo, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8280-5190","authenticated-orcid":false,"given":"Hongyan","family":"Li","sequence":"additional","affiliation":[{"name":"Geely AI Lab, Ningbo, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2484-5345","authenticated-orcid":false,"given":"Luo","family":"Ji","sequence":"additional","affiliation":[{"name":"Geely AI Lab, Ningbo, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,15]]},"reference":[{"key":"e_1_3_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-acl.225"},{"key":"e_1_3_3_2_3_2","doi-asserted-by":"publisher","unstructured":"Anna Dawid and Yann LeCun. 2024. Introduction to latent variable energy-based models: a path toward autonomous machine intelligence. Journal of Statistical Mechanics: Theory and Experiment 2024 10 (31 Oct. 2024). 10.1088\/1742-5468\/ad292b","DOI":"10.1088\/1742-5468\/ad292b"},{"key":"e_1_3_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671470"},{"key":"e_1_3_3_2_5_2","unstructured":"Yunfan Gao Yun Xiong Xinyu Gao Kangxiang Jia Jinliu Pan Yuxi Bi Yi Dai Jiawei Sun Meng Wang and Haofen Wang. 2024. Retrieval-Augmented Generation for Large Language Models: A Survey. arxiv:https:\/\/arXiv.org\/abs\/2312.10997\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2312.10997"},{"key":"e_1_3_3_2_6_2","volume-title":"The Twelfth International Conference on Learning Representations","author":"Gu Yuxian","year":"2024","unstructured":"Yuxian Gu, Li Dong, Furu Wei, and Minlie Huang. 2024. MiniLLM: Knowledge Distillation of Large Language Models. In The Twelfth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=5h0qf7IBZZ"},{"key":"e_1_3_3_2_7_2","volume-title":"arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2207.05608","author":"Huang Wenlong","year":"2022","unstructured":"Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tompson, Igor Mordatch, Yevgen Chebotar, Pierre Sermanet, Noah Brown, Tomas Jackson, Linda Luu, Sergey Levine, Karol Hausman, and Brian Ichter. 2022. Inner Monologue: Embodied Reasoning through Planning with Language Models. In arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2207.05608."},{"key":"e_1_3_3_2_8_2","first-page":"287","volume-title":"Proceedings of The 6th Conference on Robot Learning","volume":"205","author":"al. ichter\u00a0et","year":"2023","unstructured":"ichter\u00a0et al.2023. Do As I Can, Not As I Say: Grounding Language in Robotic Affordances. In Proceedings of The 6th Conference on Robot Learning , Vol.\u00a0205. PMLR, 287\u2013318. https:\/\/proceedings.mlr.press\/v205\/ichter23a.html"},{"key":"e_1_3_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-88714-7_27"},{"key":"e_1_3_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3600006.3613165"},{"key":"e_1_3_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.191"},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.269"},{"key":"e_1_3_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657951"},{"key":"e_1_3_3_2_14_2","unstructured":"Amir\u00a0M. Mansourian Rozhan Ahmadi Masoud Ghafouri Amir\u00a0Mohammad Babaei Elaheh\u00a0Badali Golezani Zeynab yasamani ghamchi Vida Ramezanian Alireza Taherian Kimia Dinashi Amirali Miri and Shohreh Kasaei. 2025. A Comprehensive Survey on Knowledge Distillation. Transactions on Machine Learning Research (2025)."},{"key":"e_1_3_3_2_15_2","unstructured":"Niklas Muennighoff. 2022. SGPT: GPT Sentence Embeddings for Semantic Search. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2202.08904 (2022)."},{"key":"e_1_3_3_2_16_2","unstructured":"Niklas Muennighoff Hongjin Su Liang Wang Nan Yang Furu Wei Tao Yu Amanpreet Singh and Douwe Kiela. 2024. Generative Representational Instruction Tuning. arxiv:https:\/\/arXiv.org\/abs\/2402.09906\u00a0[cs.CL]"},{"key":"e_1_3_3_2_17_2","unstructured":"Tri Nguyen Mir Rosenberg Xia Song Jianfeng Gao Saurabh Tiwary Rangan Majumder and Li Deng. 2016. MS MARCO: A Human Generated MAchine Reading COmprehension Dataset. (November 2016). https:\/\/www.microsoft.com\/en-us\/research\/publication\/ms-marco-human-generated-machine-reading-comprehension-dataset\/"},{"key":"e_1_3_3_2_18_2","volume-title":"QWEN2 TECHNICAL REPORT","author":"Qwen\u00a0Team Alibaba\u00a0Group","year":"2024","unstructured":"Alibaba\u00a0Group Qwen\u00a0Team. 2024. QWEN2 TECHNICAL REPORT. Technical Report. Alibaba Group."},{"key":"e_1_3_3_2_19_2","volume-title":"Proceedings of the Conference on Robot Learning (CoRL)","author":"Ren Allen\u00a0Z.","year":"2023","unstructured":"Allen\u00a0Z. Ren, Anushri Dixit, Alexandra Bodrova, Sumeet Singh, Stephen Tu, Noah Brown, Peng Xu, Leila Takayama, Fei Xia, Jake Varley, Zhenjia Xu, Dorsa Sadigh, Andy Zeng, and Anirudha Majumdar. 2023. Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners. In Proceedings of the Conference on Robot Learning (CoRL)."},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-acl.71"},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-acl.130"},{"key":"e_1_3_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.923"},{"key":"e_1_3_3_2_23_2","unstructured":"Nandan Thakur Nils Reimers Andreas R\u00fcckl\u00e9 Abhishek Srivastava and Iryna Gurevych. 2021. Beir: A heterogenous benchmark for zero-shot evaluation of information retrieval models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2104.08663 (2021)."},{"key":"e_1_3_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.642"},{"key":"e_1_3_3_2_25_2","unstructured":"Orion Weller Benjamin\u00a0Van Durme Dawn Lawrie Ashwin Paranjape Yuhao Zhang and Jack Hessel. 2024. Promptriever: Instruction-Trained Retrievers Can Be Prompted Like Language Models. arxiv:https:\/\/arXiv.org\/abs\/2409.11136\u00a0[cs.IR] https:\/\/arxiv.org\/abs\/2409.11136"},{"key":"e_1_3_3_2_26_2","unstructured":"Jintian Zhang Cheng Peng Mengshu Sun Xiang Chen Lei Liang Zhiqiang Zhang Jun Zhou Huajun Chen and Ningyu Zhang. 2024. OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs. arxiv:https:\/\/arXiv.org\/abs\/2409.05152\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2409.05152"},{"key":"e_1_3_3_2_27_2","unstructured":"Penghao Zhao Hailin Zhang Qinhan Yu Zhengren Wang Yunteng Geng Fangcheng Fu Ling Yang Wentao Zhang Jie Jiang and Bin Cui. 2024. Retrieval-Augmented Generation for AI-Generated Content: A Survey. arxiv:https:\/\/arXiv.org\/abs\/2402.19473\u00a0[cs.CV] https:\/\/arxiv.org\/abs\/2402.19473"},{"key":"e_1_3_3_2_28_2","unstructured":"Siyun Zhao Yuqing Yang Zilong Wang Zhiyuan He Luna\u00a0K. Qiu and Lili Qiu. 2024. Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely. arxiv:https:\/\/arXiv.org\/abs\/2409.14924\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2409.14924"},{"key":"e_1_3_3_2_29_2","unstructured":"Yaowei Zheng Richong Zhang Junhao Zhang Yanhan Ye Zheyan Luo and Yongqiang Ma. 2024. LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2403.13372 (2024). http:\/\/arxiv.org\/abs\/2403.13372"}],"event":{"name":"ICMR '26: International Conference on Multimedia Retrieval","location":"Amsterdam The Netherlands","acronym":"ICMR '26","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 2026 International Conference on Multimedia Retrieval"],"original-title":[],"deposited":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T14:47:37Z","timestamp":1781534857000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3805622.3810780"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,15]]},"references-count":28,"alternative-id":["10.1145\/3805622.3810780","10.1145\/3805622"],"URL":"https:\/\/doi.org\/10.1145\/3805622.3810780","relation":{},"subject":[],"published":{"date-parts":[[2026,6,15]]},"assertion":[{"value":"2026-06-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}