arXiv Computer Vision
Think Before You Score: Thinking Reward Model for Visual Generation
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Aug 28
RubricRM: Generative Reward Modeling via Dynamic Rubrics for Image Generation and Editing
RubricRM introduces a pairwise generative reward modeling framework that generates an input‑specific rubric—comprising evaluation dimensions, weights, and scoring criteria—to score candidate images. The method is trained in two stages: supervised fine‑tuning to learn the rubric‑based scoring paradigm and GRPO to refine dimension‑level rewards. Experiments on text‑to‑image generation and instruction‑based image editing benchmarks demonstrate that RubricRM outperforms existing specialized reward models and competes with strong proprietary MLLM judges while using smaller backbones.
arXiv Computer Vision
Sep 7
Learning to Credit the Right Steps: Objective-aware Process Optimization for Visual Generation
The paper introduces Objective-aware Trajectory Credit Assignment (OTCA), a framework that refines reinforcement learning for diffusion-based visual generation. OTCA decomposes credit across denoising steps and allocates multiple reward signals adaptively, addressing the coarse, uniform reward assignment of existing GRPO pipelines. Experiments demonstrate that OTCA consistently enhances image and video generation quality across various metrics.
arXiv AI
Jun 26
Joint Reward Modeling: Internalizing Chain-of-Thought for Efficient Visual Reward Models
arXiv:2602. 07533v2 Announce Type: replace Abstract: Reward models are critical for reinforcement learning from human feedback, as they determine the alignment quality and reliability of generative models.
arXiv Computer Vision
Sep 22
RewardVerse: Rubric-Guided Policy Optimization for Video Reward Modeling
arXiv:2609.22947v1 Announce Type: new Abstract: Reinforcement learning (RL) is vital for optimizing video generation models, with a robust reward model (RM) serving as the cornerstone. However, exist...
Hugging Face Trending Papers
Sep 19
RewardVerse: Rubric-Guided Policy Optimization for Video Reward Modeling
Reinforcement learning (RL) is vital for optimizing video generation models, with a robust reward model (RM) serving as the cornerstone. However, existing video reward models often produce unstable sc...
arXiv Computer Vision
Sep 3
GDB-Reward: From Evaluation Metrics to Training Rewards for Graphic Design
GDB-Reward is a framework that converts diverse graphic design evaluation metrics into a single reinforcement learning reward, enabling prompt optimization for frozen image generators. By using these metrics as scalar rewards, the method improves adherence to design specifications in perceptual quality, rendering fidelity, and spatial accuracy without fine‑tuning the generator. Experiments show that non‑differentiable metrics can serve as effective optimization objectives in settings lacking differentiable supervision.