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 - Long Grounded Thoughts: Distilling Compositional Visual Reasoning Chains at Scale
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In this work, we introduce a new reasoning data generation framework spanning diverse skills and levels of complexity with over 1M high-quality synthetic vision-centric questions. The dataset also includes preference data and instruction prompts supporting both offline and online RL. Our synthesis framework proceeds in two stages: (1) scale; and (2) complexity. Reasoning traces are then synthesized through a two-stage process that leverages VLMs and reasoning LLMs, producing CoT traces for VLMs that capture the richness and diverse cognitive behaviors found in frontier reasoning models. Remarkably, we show that finetuning Qwen2.5-VL-7B on our data outperforms all open-data baselines across all evaluated vision-centric benchmarks, and even surpasses strong closed-data models such as MiMo-VL-7B-RL on V* Bench, CV-Bench and MMStar-V. Perhaps most surprising, despite being entirely vision-centric, our data transfers positively to text-only reasoning (MMLU-Pro) and audio reasoning (MMAU), demonstrating its effectiveness. Similarly, despite not containing videos or embodied visual data, we observe notable gains when evaluating on a single-evidence embodied QA benchmark (NiEH). Finally, we use our data to analyze the entire VLM post-training pipeline. Our empirical analysis highlights that (i) SFT on high-quality data with non-linear reasoning traces is essential for effective online RL, (ii) staged offline RL matches online RL's performance while reducing compute demands, and (iii) careful SFT on high quality data can substantially improve out-of-domain, cross-modality transfer.","upvotes":8,"discussionId":"6913700aa644ba07c499c931","ai_summary":"A new reasoning data generation framework creates a large-scale vision-centric dataset with over 1M synthetic questions, enhancing performance across various benchmarks and improving cross-modality transfer.","ai_keywords":["multimodal reasoning","reasoning data generation framework","synthetic vision-centric questions","preference data","instruction prompts","offline RL","online RL","VLMs","reasoning LLMs","CoT traces","finetuning","Qwen2.5-VL-7B","MiMo-VL-7B-RL","V* Bench","CV-Bench","MMStar-V","MMLU-Pro","MMAU","NiEH","SFT","staged offline RL","out-of-domain","cross-modality transfer"],"organization":{"_id":"60262b67268c201cdc8b7d43","name":"nvidia","fullname":"NVIDIA","avatar":"https://cdn-uploads.huggingface.co/production/uploads/1613114437487-60262a8e0703121c822a80b6.png"}},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"629e1b71bb6419817ed7566c","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/629e1b71bb6419817ed7566c/0ZCt-11eQtRDCOk9AozOp.jpeg","isPro":false,"fullname":"Huck Yang","user":"huckiyang","type":"user"},{"_id":"61703fa3dff0ef663e421ab5","avatarUrl":"/avatars/96172e2782e218bbbddfdf47f96c1ad4.svg","isPro":false,"fullname":"Jaehun Jung","user":"Jaehun","type":"user"},{"_id":"67ad9767d3a5cc6789882e10","avatarUrl":"/avatars/e5d16dc828670963ffe3bf2cf33318ab.svg","isPro":false,"fullname":"Kim Jiwan","user":"JiwanKim","type":"user"},{"_id":"6434b6619bd5a84b5dcfa4de","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6434b6619bd5a84b5dcfa4de/h8Q6kPNjFNc03wmdboHzq.jpeg","isPro":true,"fullname":"Young-Jun Lee","user":"passing2961","type":"user"},{"_id":"631e14ac473a6825f285e89d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/631e14ac473a6825f285e89d/K-6QnoeGLg8XFvbTMMdqA.jpeg","isPro":false,"fullname":"Yury Panikov","user":"panikov","type":"user"},{"_id":"686db5d4af2b856fabbf13aa","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/6BjMv2LVNoqvbX8fQSTPI.png","isPro":false,"fullname":"V bbbb","user":"Bbbbbnnn","type":"user"},{"_id":"63c1699e40a26dd2db32400d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/63c1699e40a26dd2db32400d/3N0-Zp8igv8-52mXAdiiq.jpeg","isPro":false,"fullname":"Chroma","user":"Chroma111","type":"user"},{"_id":"645abb43c4acfcf6640270e3","avatarUrl":"/avatars/e65f8de9b9fe878bd8e9106a802c6f21.svg","isPro":false,"fullname":"David Acuna","user":"davidjesusacu","type":"user"}],"acceptLanguages":["*"],"dailyPaperRank":0,"organization":{"_id":"60262b67268c201cdc8b7d43","name":"nvidia","fullname":"NVIDIA","avatar":"https://cdn-uploads.huggingface.co/production/uploads/1613114437487-60262a8e0703121c822a80b6.png"}}">
A new reasoning data generation framework creates a large-scale vision-centric dataset with over 1M synthetic questions, enhancing performance across various benchmarks and improving cross-modality transfer.
AI-generated summary
Recent progress in multimodal reasoning has been driven largely by undisclosed datasets and proprietary data synthesis recipes, leaving open questions about how to systematically build large-scale, vision-centric reasoning datasets, particularly for tasks that go beyond visual math. In this work, we introduce a new reasoning data generation framework spanning diverse skills and levels of complexity with over 1M high-quality synthetic vision-centric questions. The dataset also includes preference data and instruction prompts supporting both offline and online RL. Our synthesis framework proceeds in two stages: (1) scale; and (2) complexity. Reasoning traces are then synthesized through a two-stage process that leverages VLMs and reasoning LLMs, producing CoT traces for VLMs that capture the richness and diverse cognitive behaviors found in frontier reasoning models. Remarkably, we show that finetuningQwen2.5-VL-7B on our data outperforms all open-data baselines across all evaluated vision-centric benchmarks, and even surpasses strong closed-data models such as MiMo-VL-7B-RL on V* Bench, CV-Bench and MMStar-V. Perhaps most surprising, despite being entirely vision-centric, our data transfers positively to text-only reasoning (MMLU-Pro) and audio reasoning (MMAU), demonstrating its effectiveness. Similarly, despite not containing videos or embodied visual data, we observe notable gains when evaluating on a single-evidence embodied QA benchmark (NiEH). Finally, we use our data to analyze the entire VLM post-training pipeline. Our empirical analysis highlights that (i) SFT on high-quality data with non-linear reasoning traces is essential for effective online RL, (ii) staged offline RL matches online RL's performance while reducing compute demands, and (iii) careful SFT on high quality data can substantially improve out-of-domain, cross-modality transfer.