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Long-context retrieval success is not broad long-document reasoning reliability. Model sizes and training stages remain one contribution.", "domain": "Language-model training and open research resources" }, "sources": [ { "citation": "InternLM2 Technical Report", "url": "https://arxiv.org/abs/2403.17297v1" }, { "citation": "Cai et al.: InternLM2 conditional online preference training — author/company record", "url": "https://github.com/InternLM/InternLM" } ], "statement": "# Cai et al.: InternLM2 conditional online preference training\n\nDocumented a long-context language-model training pipeline and conditional online RLHF designed to handle conflicting preferences and reward hacking, releasing intermediate model stages.\n\n## Scientific scope\n\nThe source reports empirical mitigation, not elimination of reward hacking or universal preference compatibility. Long-context retrieval success is not broad long-document reasoning reliability. Model sizes and training stages remain one contribution.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nZheng Cai; Maosong Cao; Haojiong Chen; Kai Chen; Keyu Chen; Xin Chen; Xun Chen; Zehui Chen; Zhi Chen; Pei Chu; Xiaoyi Dong; Haodong Duan; Qi Fan; Zhaoye Fei; Yang Gao; Jiaye Ge; Chenya Gu; Yuzhe Gu; Tao Gui; Aijia Guo; Qipeng Guo; Conghui He; Yingfan Hu; Ting Huang; Tao Jiang; Penglong Jiao; Zhenjiang Jin; Zhikai Lei; Jiaxing Li; Jingwen Li; Linyang Li; Shuaibin Li; Wei Li; Yining Li; Hongwei Liu; Jiangning Liu; Jiawei Hong; Kaiwen Liu; Kuikun Liu; Xiaoran Liu; Chengqi Lv; Haijun Lv; Kai Lv; Li Ma; Runyuan Ma; Zerun Ma; Wenchang Ning; Linke Ouyang; Jiantao Qiu; Yuan Qu; Fukai Shang; Yunfan Shao; Demin Song; Zifan Song; Zhihao Sui; Peng Sun; Yu Sun; Huanze Tang; Bin Wang; Guoteng Wang; Jiaqi Wang; Jiayu Wang; Rui Wang; Yudong Wang; Ziyi Wang; Xingjian Wei; Qizhen Weng; Fan Wu; Yingtong Xiong; Chao Xu; Ruiliang Xu; Hang Yan; Yirong Yan; Xiaogui Yang; Haochen Ye; Huaiyuan Ying; Jia Yu; Jing Yu; Yuhang Zang; Chuyu Zhang; Li Zhang; Pan Zhang; Peng Zhang; Ruijie Zhang; Shuo Zhang; Songyang Zhang; Wenjian Zhang; Wenwei Zhang; Xingcheng Zhang; Xinyue Zhang; Hui Zhao; Qian Zhao; Xiaomeng Zhao; Fengzhe Zhou; Zaida Zhou; Jingming Zhuo; Yicheng Zou; Xipeng Qiu; Yu Qiao; Dahua Lin\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [InternLM2 Technical Report](https://arxiv.org/abs/2403.17297v1)\n- [Cai et al.: InternLM2 conditional online preference training — author/company record](https://github.com/InternLM/InternLM)\n", "summary": "Documented a long-context language-model training pipeline and conditional online RLHF designed to handle conflicting preferences and reward hacking, releasing intermediate model stages.", "title": "Cai et al.: InternLM2 conditional online preference training" }, "record_id": "urn:uuid:03c1f809-d40d-4095-aa2a-483b23b81ddb", "version": 1, "source_ref": { "record_id": "urn:uuid:03c1f809-d40d-4095-aa2a-483b23b81ddb", "version": 1, "content_hash": "sha256:184331e7b4aabe08194e69139f864ad8aa9a00c7c369358aad576b09df53f816" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:ea4a1dba2d3e7807367ef12129664b9116f6f2337b596f6955099f0bcab5959b" }, "target": { "record_id": "urn:uuid:03c1f809-d40d-4095-aa2a-483b23b81ddb", "version": 1, "content_hash": "sha256:184331e7b4aabe08194e69139f864ad8aa9a00c7c369358aad576b09df53f816" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "cai-et-al-internlm2-cool-rlhf-2024", "entry_id": "cai-et-al-internlm2-cool-rlhf-2024", "revision": 1, "title": "Cai et al.: InternLM2 conditional online preference training", "context": "Language-model training and open research resources", "track": "Human-designed research method or resource", "band": "B", "modifier": "+", "status": "PROVISIONAL_EDITORIAL", "assessed_on": "2026-10-16", "evidence_cutoff": "2026-10-16", "achievement_state": "SUPPORTED_SCOPED_RESULT", "what_changed": "Documented a long-context language-model training pipeline and conditional online RLHF designed to handle conflicting preferences and reward hacking, releasing intermediate model stages.", "scope": "Documented a long-context language-model training pipeline and conditional online RLHF designed to handle conflicting preferences and reward hacking, releasing intermediate model stages. 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