AR-GRPO: Training Autoregressive Image Generation Models via Reinforcement Learning

• URL: http://arxiv.org/abs/2508.06924v1
• Date: Sat, 09 Aug 2025 10:37:26 GMT
• Title: AR-GRPO: Training Autoregressive Image Generation Models via Reinforcement Learning
• Authors: Shihao Yuan, Yahui Liu, Yang Yue, Jingyuan Zhang, Wangmeng Zuo, Qi Wang, Fuzheng Zhang, Guorui Zhou,
• Abstract summary: We propose AR-GRPO, an approach to integrate online RL training into autoregressive (AR) image generation models.<n>We conduct comprehensive experiments on both class-conditional (i.e., class-to-image) and text-conditional (i.e., text-to-image) image generation tasks.<n>Our results show consistent improvements across various evaluation metrics.
• Score: 56.71089466532673
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inspired by the success of reinforcement learning (RL) in refining large language models (LLMs), we propose AR-GRPO, an approach to integrate online RL training into autoregressive (AR) image generation models. We adapt the Group Relative Policy Optimization (GRPO) algorithm to refine the vanilla autoregressive models' outputs by carefully designed reward functions that evaluate generated images across multiple quality dimensions, including perceptual quality, realism, and semantic fidelity. We conduct comprehensive experiments on both class-conditional (i.e., class-to-image) and text-conditional (i.e., text-to-image) image generation tasks, demonstrating that our RL-enhanced framework significantly improves both the image quality and human preference of generated images compared to the standard AR baselines. Our results show consistent improvements across various evaluation metrics, establishing the viability of RL-based optimization for AR image generation and opening new avenues for controllable and high-quality image synthesis. The source codes and models are available at: https://github.com/Kwai-Klear/AR-GRPO.

Related papers

• RL-RIG: A Generative Spatial Reasoner via Intrinsic Reflection [18.52946282633359]
  RL-RIG is a Reinforcement Learning framework for Reflection-based Image Generation.<n>We develop Reflection-GRPO to train the VLM Actor for edit prompts and the Image Editor for better image quality under a given prompt.<n> Experimental results show that RL-RIG outperforms existing state-of-the-art open-source models by up to 11% in terms of controllable and precise spatial reasoning in image generation.
  arXiv  Detail & Related papers  (2026-02-23T15:39:53Z)
• ReasonGen-R1: CoT for Autoregressive Image generation models through SFT and RL [54.100889131719626]
  Chain-of-thought reasoning and reinforcement learning have driven breakthroughs in NLP.<n>We introduce ReasonGen-R1, a framework that imbues an autoregressive image generator with explicit text-based "thinking" skills.<n>We show that ReasonGen-R1 consistently outperforms strong baselines and prior state-of-the-art models.
  arXiv  Detail & Related papers  (2025-05-30T17:59:48Z)
• Fine-Tuning Next-Scale Visual Autoregressive Models with Group Relative Policy Optimization [1.1510009152620668]
  Fine-tuning pre-trained generative models with Reinforcement Learning (RL) has emerged as an effective approach for aligning outputs with human preferences.<n>We show that RL-based fine-tuning is both efficient and effective for VAR models, benefiting particularly from their fast inference speeds.
  arXiv  Detail & Related papers  (2025-05-29T10:45:38Z)
• Policy Optimized Text-to-Image Pipeline Design [72.87655664038617]
  We introduce a novel reinforcement learning-based framework for text-to-image generation.<n>Our approach first trains an ensemble of reward models capable of predicting image quality scores directly from prompt-workflow combinations.<n>We then implement a two-phase training strategy: initial vocabulary training followed by GRPO-based optimization.
  arXiv  Detail & Related papers  (2025-05-27T17:50:47Z)
• Delving into RL for Image Generation with CoT: A Study on DPO vs. GRPO [68.44918104224818]
  Autoregressive image generation presents unique challenges distinct from Chain-of-Thought (CoT) reasoning.<n>This study provides the first comprehensive investigation of the GRPO and DPO algorithms in autoregressive image generation.<n>Our findings reveal that GRPO and DPO exhibit distinct advantages, and crucially, that reward models possessing stronger intrinsic generalization capabilities potentially enhance the generalization potential of the applied RL algorithms.
  arXiv  Detail & Related papers  (2025-05-22T17:59:49Z)
• Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step [86.69947123512836]
  Chain-of-Thought (CoT) reasoning has been extensively explored in large models to tackle complex understanding tasks.<n>We provide the first comprehensive investigation of the potential of CoT reasoning to enhance autoregressive image generation.<n>We propose the Potential Assessment Reward Model (PARM) and PARM++, specialized for autoregressive image generation.
  arXiv  Detail & Related papers  (2025-01-23T18:59:43Z)
• RL for Consistency Models: Faster Reward Guided Text-to-Image Generation [15.238373471473645]
  We propose a framework for fine-tuning consistency models viaReinforcement Learning (RL) Our framework, called Reinforcement Learning for Consistency Model (RLCM), frames the iterative inference process of a consistency model as an RL procedure. Comparing to RL finetuned diffusion models, RLCM trains significantly faster, improves the quality of the generation measured under the reward objectives, and speeds up the inference procedure by generating high quality images with as few as two inference steps.
  arXiv  Detail & Related papers  (2024-03-25T15:40:22Z)
• A Generic Approach for Enhancing GANs by Regularized Latent Optimization [79.00740660219256]
  We introduce a generic framework called em generative-model inference that is capable of enhancing pre-trained GANs effectively and seamlessly. Our basic idea is to efficiently infer the optimal latent distribution for the given requirements using Wasserstein gradient flow techniques.
  arXiv  Detail & Related papers  (2021-12-07T05:22:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.