Deprecated: The each() function is deprecated. This message will be suppressed on further calls in /home/zhenxiangba/zhenxiangba.com/public_html/phproxy-improved-master/index.php on line 456 Paper page - Skywork R1V: Pioneering Multimodal Reasoning with Chain-of-Thought
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Leveraging a lightweight visual projector, Skywork\nR1V facilitates seamless multimodal adaptation without necessitating retraining\nof either the foundational language model or the vision encoder. To strengthen\nvisual-text alignment, we propose a hybrid optimization strategy that combines\nIterative Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization\n(GRPO), significantly enhancing cross-modal integration efficiency.\nAdditionally, we introduce an adaptive-length Chain-of-Thought distillation\napproach for reasoning data generation. This approach dynamically optimizes\nreasoning chain lengths, thereby enhancing inference efficiency and preventing\nexcessive reasoning overthinking. Empirical evaluations demonstrate that\nSkywork R1V, with only 38B parameters, delivers competitive performance,\nachieving a score of 69.0 on the MMMU benchmark and 67.5 on MathVista.\nMeanwhile, it maintains robust textual reasoning performance, evidenced by\nimpressive scores of 72.0 on AIME and 94.0 on MATH500. The Skywork R1V model\nweights have been publicly released to promote openness and reproducibility.","upvotes":85,"discussionId":"67f61a9daf81b0685bf05731","githubRepo":"https://github.com/SkyworkAI/Skywork-R1V","githubRepoAddedBy":"user","ai_summary":"Skywork R1V extends large language models to multimodal reasoning with efficient transfer, enhanced visual-text alignment, and dynamic reasoning chain optimization, achieving competitive performance in various benchmarks.","ai_keywords":["multimodal reasoning model","R1-series Large language models","multimodal transfer method","lightweight visual projector","Iterative Supervised Fine-Tuning","Group Relative Policy Optimization","adaptive-length Chain-of-Thought distillation","MMMU benchmark","MathVista","AIME","MATH500"],"githubStars":3150,"organization":{"_id":"6522615d9334173c627b0efa","name":"Skywork","fullname":"Skywork","avatar":"https://cdn-uploads.huggingface.co/production/uploads/64535b71bcbd25618f7655da/AvtJ4GuPAyhLxl2-leVt6.jpeg"}},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"6462b241b438438da3c25a5d","avatarUrl":"/avatars/606a67f1be639c9a5e36f293abd5f27a.svg","isPro":false,"fullname":"Xuchen Song","user":"xuchensong","type":"user"},{"_id":"619b03a080ebe7c9091fbf3c","avatarUrl":"/avatars/0b4be841601195cc73d984055ffab565.svg","isPro":false,"fullname":"Hu Dou Dou","user":"hl0737","type":"user"},{"_id":"612cfc6e1f69b222aacf831b","avatarUrl":"/avatars/b6c7d15ebc7b5dd4b56620bfab324c77.svg","isPro":false,"fullname":"lycfight","user":"lycfight","type":"user"},{"_id":"620f5a1c3f76c50e6458a9b6","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/620f5a1c3f76c50e6458a9b6/pXh_f5F0UvufxuUa-eS-v.jpeg","isPro":true,"fullname":"Peiyu Wang","user":"OrlandoHugBot","type":"user"},{"_id":"63fdb1aa27abbe6b3ce098f5","avatarUrl":"/avatars/c22e3a77ff84b3b87c16cff2469f6d3d.svg","isPro":false,"fullname":"xietian","user":"sealical","type":"user"},{"_id":"673f0a5bcdad8a9744d17df0","avatarUrl":"/avatars/413b0472c9790395a64aafe9294143bd.svg","isPro":false,"fullname":"Yichen Wei","user":"yichenchenchen","type":"user"},{"_id":"653dd16277c2f09452ad37cd","avatarUrl":"/avatars/a95f9527722845a5414d86180c8e945d.svg","isPro":false,"fullname":"Yunzhuo Hao","user":"luckychao","type":"user"},{"_id":"62be9b5aae56e75e4d689e7c","avatarUrl":"/avatars/6772bc09d6eeb4e86b1210481be91720.svg","isPro":false,"fullname":"wangxiaokun","user":"shawn0wang","type":"user"},{"_id":"67c8145f5999e7df91a2f8b8","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/nAtdMj3n1dV8IkhcPNeUe.png","isPro":false,"fullname":"skyipeng","user":"skyipeng","type":"user"},{"_id":"660aab2c878289c5b34f9e97","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/660aab2c878289c5b34f9e97/yxx1-lR8x5o6KaEpZDXQq.jpeg","isPro":false,"fullname":"weijie qiu","user":"qiuwj","type":"user"},{"_id":"620783f24e28382272337ba4","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/620783f24e28382272337ba4/zkUveQPNiDfYjgGhuFErj.jpeg","isPro":false,"fullname":"GuoLiangTang","user":"Tommy930","type":"user"},{"_id":"67f61e1459f6e8c3698a84a9","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/eRTXFseUr13f-aaY3LXgn.png","isPro":false,"fullname":"peng bin","user":"pengdott","type":"user"}],"acceptLanguages":["*"],"dailyPaperRank":3,"organization":{"_id":"6522615d9334173c627b0efa","name":"Skywork","fullname":"Skywork","avatar":"https://cdn-uploads.huggingface.co/production/uploads/64535b71bcbd25618f7655da/AvtJ4GuPAyhLxl2-leVt6.jpeg"}}">
Skywork R1V extends large language models to multimodal reasoning with efficient transfer, enhanced visual-text alignment, and dynamic reasoning chain optimization, achieving competitive performance in various benchmarks.
AI-generated summary
We introduce Skywork R1V, a multimodal reasoning model extending the an
R1-series Large language models (LLM) to visual modalities via an efficient
multimodal transfer method. Leveraging a lightweight visual projector, Skywork
R1V facilitates seamless multimodal adaptation without necessitating retraining
of either the foundational language model or the vision encoder. To strengthen
visual-text alignment, we propose a hybrid optimization strategy that combines
Iterative Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization
(GRPO), significantly enhancing cross-modal integration efficiency.
Additionally, we introduce an adaptive-length Chain-of-Thought distillation
approach for reasoning data generation. This approach dynamically optimizes
reasoning chain lengths, thereby enhancing inference efficiency and preventing
excessive reasoning overthinking. Empirical evaluations demonstrate that
Skywork R1V, with only 38B parameters, delivers competitive performance,
achieving a score of 69.0 on the MMMU benchmark and 67.5 on MathVista.
Meanwhile, it maintains robust textual reasoning performance, evidenced by
impressive scores of 72.0 on AIME and 94.0 on MATH500. The Skywork R1V model
weights have been publicly released to promote openness and reproducibility.
Skywork R1V: an open-sourced 38B multimodal reasoning model extending R1-series LLMs to vision via efficient transfer, hybrid SFT+GRPO training, and adaptive CoT distillation—69.0 on MMMU, 67.5 on MathVista, with strong math reasoning. Model weights are open-sourced! #AI #LLM #Multimodal