MindGYM: What Matters in Question Synthesis for Thinking-Centric Fine-Tuning?
Fuente:
arXiv
Saved in:
| Main Authors: | Xu, Zhe, Chen, Daoyuan, Ling, Zhenqing, Li, Yaliang, Shen, Ying |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
On the Convergence of Zeroth-Order Federated Tuning for Large Language Models
by: Ling, Zhenqing, et al.
Published: (2024)
by: Ling, Zhenqing, et al.
Published: (2024)
Diversity as a Reward: Fine-Tuning LLMs on a Mixture of Domain-Undetermined Data
by: Ling, Zhenqing, et al.
Published: (2025)
by: Ling, Zhenqing, et al.
Published: (2025)
HumanVBench: Probing Human-Centric Video Understanding in MLLMs with Automatically Synthesized Benchmarks
by: Zhou, Ting, et al.
Published: (2024)
by: Zhou, Ting, et al.
Published: (2024)
VeriSciQA: An Auto-Verified Dataset for Scientific Visual Question Answering
by: Li, Yuyi, et al.
Published: (2025)
by: Li, Yuyi, et al.
Published: (2025)
Closing the Gap: Data-Centric Fine-Tuning of Vision Language Models for the Standardized Exam Questions
by: Sert, Egemen, et al.
Published: (2025)
by: Sert, Egemen, et al.
Published: (2025)
Img-Diff: Contrastive Data Synthesis for Multimodal Large Language Models
by: Jiao, Qirui, et al.
Published: (2024)
by: Jiao, Qirui, et al.
Published: (2024)
From Training-Free to Adaptive: Empirical Insights into MLLMs' Understanding of Detection Information
by: Jiao, Qirui, et al.
Published: (2024)
by: Jiao, Qirui, et al.
Published: (2024)
AttriCtrl: Fine-Grained Control of Aesthetic Attribute Intensity in Diffusion Models
by: Chen, Die, et al.
Published: (2025)
by: Chen, Die, et al.
Published: (2025)
Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey
by: Kuang, Jiayi, et al.
Published: (2024)
by: Kuang, Jiayi, et al.
Published: (2024)
AutoLoRA: Automatic LoRA Retrieval and Fine-Grained Gated Fusion for Text-to-Image Generation
by: Li, Zhiwen, et al.
Published: (2025)
by: Li, Zhiwen, et al.
Published: (2025)
Less is More: High-value Data Selection for Visual Instruction Tuning
by: Liu, Zikang, et al.
Published: (2024)
by: Liu, Zikang, et al.
Published: (2024)
DetailMaster: Can Your Text-to-Image Model Handle Long Prompts?
by: Jiao, Qirui, et al.
Published: (2025)
by: Jiao, Qirui, et al.
Published: (2025)
Do we Really Need Visual Instructions? Towards Visual Instruction-Free Fine-tuning for Large Vision-Language Models
by: Liu, Zikang, et al.
Published: (2025)
by: Liu, Zikang, et al.
Published: (2025)
Expert Pyramid Tuning: Efficient Parameter Fine-Tuning for Expertise-Driven Task Allocation
by: Zhang, Jia-Chen, et al.
Published: (2026)
by: Zhang, Jia-Chen, et al.
Published: (2026)
LLAVADI: What Matters For Multimodal Large Language Models Distillation
by: Xu, Shilin, et al.
Published: (2024)
by: Xu, Shilin, et al.
Published: (2024)
EVQAScore: A Fine-grained Metric for Video Question Answering Data Quality Evaluation
by: Liang, Hao, et al.
Published: (2024)
by: Liang, Hao, et al.
Published: (2024)
Learning Domain Knowledge in Multimodal Large Language Models through Reinforcement Fine-Tuning
by: Cao, Qinglong, et al.
Published: (2026)
by: Cao, Qinglong, et al.
Published: (2026)
Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning
by: Xu, Zhiyang, et al.
Published: (2024)
by: Xu, Zhiyang, et al.
Published: (2024)
ArtAug: Enhancing Text-to-Image Generation through Synthesis-Understanding Interaction
by: Duan, Zhongjie, et al.
Published: (2024)
by: Duan, Zhongjie, et al.
Published: (2024)
SAIL-RL: Guiding MLLMs in When and How to Think via Dual-Reward RL Tuning
by: Shu, Fangxun, et al.
Published: (2025)
by: Shu, Fangxun, et al.
Published: (2025)
MMToM-QA: Multimodal Theory of Mind Question Answering
by: Jin, Chuanyang, et al.
Published: (2024)
by: Jin, Chuanyang, et al.
Published: (2024)
UNIDOC-BENCH: A Unified Benchmark for Document-Centric Multimodal RAG
by: Peng, Xiangyu, et al.
Published: (2025)
by: Peng, Xiangyu, et al.
Published: (2025)
SemiHVision: Enhancing Medical Multimodal Models with a Semi-Human Annotated Dataset and Fine-Tuned Instruction Generation
by: Wang, Junda, et al.
Published: (2024)
by: Wang, Junda, et al.
Published: (2024)
Ask Questions with Double Hints: Visual Question Generation with Answer-awareness and Region-reference
by: Shen, Kai, et al.
Published: (2024)
by: Shen, Kai, et al.
Published: (2024)
Dynamic Embedding of Hierarchical Visual Features for Efficient Vision-Language Fine-Tuning
by: Wei, Xinyu, et al.
Published: (2025)
by: Wei, Xinyu, et al.
Published: (2025)
Mask What Matters: Mitigating Object Hallucinations in Multimodal Large Language Models with Object-Aligned Visual Contrastive Decoding
by: Chen, Boqi, et al.
Published: (2026)
by: Chen, Boqi, et al.
Published: (2026)
Joint Extraction Matters: Prompt-Based Visual Question Answering for Multi-Field Document Information Extraction
by: Loem, Mengsay, et al.
Published: (2025)
by: Loem, Mengsay, et al.
Published: (2025)
Task-Specific Directions: Definition, Exploration, and Utilization in Parameter Efficient Fine-Tuning
by: Si, Chongjie, et al.
Published: (2024)
by: Si, Chongjie, et al.
Published: (2024)
Beyond Accuracy Optimization: Computer Vision Losses for Large Language Model Fine-Tuning
by: Cambrin, Daniele Rege, et al.
Published: (2024)
by: Cambrin, Daniele Rege, et al.
Published: (2024)
VIRAL: Visual In-Context Reasoning via Analogy in Diffusion Transformers
by: Li, Zhiwen, et al.
Published: (2026)
by: Li, Zhiwen, et al.
Published: (2026)
Learning What Matters: Dynamic Dimension Selection and Aggregation for Interpretable Vision-Language Reward Modeling
by: Chen, Qiyuan, et al.
Published: (2026)
by: Chen, Qiyuan, et al.
Published: (2026)
Visual Reasoning at Urban Intersections: FineTuning GPT-4o for Traffic Conflict Detection
by: Masri, Sari, et al.
Published: (2025)
by: Masri, Sari, et al.
Published: (2025)
Shifting AI Efficiency From Model-Centric to Data-Centric Compression
by: Liu, Xuyang, et al.
Published: (2025)
by: Liu, Xuyang, et al.
Published: (2025)
Peregrine: One-Shot Fine-Tuning for FHE Inference of General Deep CNNs
by: Ling, Huaming, et al.
Published: (2025)
by: Ling, Huaming, et al.
Published: (2025)
PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models
by: Prottasha, Nusrat Jahan, et al.
Published: (2025)
by: Prottasha, Nusrat Jahan, et al.
Published: (2025)
EVALALIGN: Supervised Fine-Tuning Multimodal LLMs with Human-Aligned Data for Evaluating Text-to-Image Models
by: Tan, Zhiyu, et al.
Published: (2024)
by: Tan, Zhiyu, et al.
Published: (2024)
Where do Large Vision-Language Models Look at when Answering Questions?
by: Xing, Xiaoying, et al.
Published: (2025)
by: Xing, Xiaoying, et al.
Published: (2025)
On Efficient Language and Vision Assistants for Visually-Situated Natural Language Understanding: What Matters in Reading and Reasoning
by: Kim, Geewook, et al.
Published: (2024)
by: Kim, Geewook, et al.
Published: (2024)
Thinking with Programming Vision: Towards a Unified View for Thinking with Images
by: Guo, Zirun, et al.
Published: (2025)
by: Guo, Zirun, et al.
Published: (2025)
Fine-tuning MLLMs Without Forgetting Is Easier Than You Think
by: Li, He, et al.
Published: (2026)
by: Li, He, et al.
Published: (2026)
Similar Items
-
On the Convergence of Zeroth-Order Federated Tuning for Large Language Models
by: Ling, Zhenqing, et al.
Published: (2024) -
Diversity as a Reward: Fine-Tuning LLMs on a Mixture of Domain-Undetermined Data
by: Ling, Zhenqing, et al.
Published: (2025) -
HumanVBench: Probing Human-Centric Video Understanding in MLLMs with Automatically Synthesized Benchmarks
by: Zhou, Ting, et al.
Published: (2024) -
VeriSciQA: An Auto-Verified Dataset for Scientific Visual Question Answering
by: Li, Yuyi, et al.
Published: (2025) -
Closing the Gap: Data-Centric Fine-Tuning of Vision Language Models for the Standardized Exam Questions
by: Sert, Egemen, et al.
Published: (2025)