VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training | Paper Citation Record

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This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 1

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training OpenAI o1 System Card

Reference 2

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This paper cites VL-RewardBench: A Challenging Benchmark for Vision-Language Generative Reward Models.

VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training VL-RewardBench: A Challenging Benchmark for Vision-Language Generative Reward Models

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This paper cites Aligning large multimodal models with factually augmented rlhf.

VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Aligning large multimodal models with factually augmented rlhf

Reference 4

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This paper cites Silkie: Preference distillation for large visual language models, 2023.

VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Silkie: Preference distillation for large visual language models, 2023

Reference 5

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This paper cites Visual-RFT: Visual Reinforcement Fine-Tuning.

VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Visual-RFT: Visual Reinforcement Fine-Tuning

Reference 6

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Rank analysis of incomplete block designs: I

Reference 7

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This paper cites Strengthening multimodal large language model with bootstrapped preference optimization, 2024 a.

VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Strengthening multimodal large language model with bootstrapped preference optimization, 2024 a

Reference 8

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This paper cites RLAIF-V : Aligning mllms through open-source ai feedback for super gpt-4v trustworthiness.

VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training RLAIF-V : Aligning mllms through open-source ai feedback for super gpt-4v trustworthiness

Reference 9

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Direct preference optimization: Your language model is secretly a reward model

Reference 10

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This paper cites Self-rewarding language models.

VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Self-rewarding language models

Reference 11

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Iterative preference learning from human feedback: Bridging theory and practice for rlhf under kl-constraint

Reference 12

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This paper cites RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback.

VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback

Reference 13

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Star: Bootstrapping reasoning with reasoning

Reference 14

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training RAFT : Reward ranked finetuning for generative foundation model alignment

Reference 15

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Rlhf workflow: From reward modeling to online rlhf

Reference 16

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This paper cites Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models.

VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models

Reference 17

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This paper cites Image Textualization: An Automatic Framework for Creating Accurate and Detailed Image Descriptions.

VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Image Textualization: An Automatic Framework for Creating Accurate and Detailed Image Descriptions

Reference 18

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training AIDE: Agentically Improve Visual Language Model with Domain Experts

Reference 19

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This paper cites Generative Verifiers: Reward Modeling as Next-Token Prediction.

VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Generative Verifiers: Reward Modeling as Next-Token Prediction

Reference 20

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training DPO Meets PPO: Reinforced Token Optimization for RLHF

Reference 21

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs

Reference 22

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Secrets of RLHF in Large Language Models Part II: Reward Modeling

Reference 23

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Aligning Modalities in Vision Large Language Models via Preference Fine-tuning

Reference 24

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Chi, Quoc V

Reference 25

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Iterative reasoning preference optimization

Reference 26

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Grounding dino: Marrying dino with grounded pre-training for open-set object detection, 2023 a

Reference 27

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Detectron2

Reference 28

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Proximal Policy Optimization Algorithms

Reference 29

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Training language models to follow instructions with human feedback

Reference 30

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 31

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Hello gpt-4o, 2024

Reference 32

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 33

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Gemini: A family of highly capable multimodal models, 2023

Reference 34

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Visual instruction tuning, 2023 b

Reference 35

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Instructblip: Towards general-purpose vision-language models with instruction tuning, 2023

Reference 36

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Llavar: Enhanced visual instruction tuning for text-rich image understanding, 2024 b

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Sharegpt4v: Improving large multi-modal models with better captions, 2023

Reference 38

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training LLaVA-OneVision: Easy Visual Task Transfer

Reference 39

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Fast r-cnn

Reference 40

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training G-detkd: towards general distillation framework for object detectors via contrastive and semantic-guided feature imitation

Reference 41

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training End-to-end object detection with transformers

Reference 42

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Global-local path networks for monocular depth estimation with vertical cutdepth, 2022

Reference 43

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Depth anything: Unleashing the power of large-scale unlabeled data, 2024 b

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Open-vocabulary Object Detection via Vision and Language Knowledge Distillation

Reference 45

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Detclipv3: Towards versatile generative open-vocabulary object detection, 2024

Reference 46

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Let's verify step by step

Reference 47

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Entropy-regularized process reward model

Reference 48

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations

Reference 49

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Judging llm-as-a-judge with mt-bench and chatbot arena

Reference 50

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training LLaVA-Critic: Learning to Evaluate Multimodal Models

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This paper cites Some things are more CRINGE than others: Iterative Preference Optimization with the Pairwise Cringe Loss.

VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Some things are more CRINGE than others: Iterative Preference Optimization with the Pairwise Cringe Loss

Reference 52

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Reinforced self-training (rest) for language modeling

Reference 53

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Self-play fine-tuning converts weak language models to strong language models, 2024 b

Reference 54

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This paper cites Lora: Low-rank adaptation of large language models.

VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Lora: Low-rank adaptation of large language models

Reference 55

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training WildVision: Evaluating Vision-Language Models in the Wild with Human Preferences

Reference 56

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training RlHF-V : Towards trustworthy mllms via behavior alignment from fine-grained correctional human feedback

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:34:05.131722Z

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This paper cites MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark.

VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark

Reference 58

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems? In European Conference on Computer Vision, pages 169--186

Reference 59

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification

Reference 60

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training V-STaR: Training Verifiers for Self-Taught Reasoners

Reference 61

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 62

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

Reference 63

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Llava-next: Improved reasoning, ocr, and world knowledge, January 2024

Reference 64

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond, 2023

Reference 65

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training The Llama 3 Herd of Models

Reference 66

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models

Reference 67

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This paper cites DeepSeek-VL: Towards Real-World Vision-Language Understanding.

VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training DeepSeek-VL: Towards Real-World Vision-Language Understanding

Reference 68

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Aria: An Open Multimodal Native Mixture-of-Experts Model

Reference 69

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale

Reference 70

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VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training Qwen2.5 Technical Report

Reference 71

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