REVIEW 11 cited by
AR-GRPO: Training Autoregressive Image Generation Models via Reinforcement Learning
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
AR-GRPO: Training Autoregressive Image Generation Models via Reinforcement Learning
read the original 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.
Forward citations
Cited by 11 Pith papers
-
JAGG: Jacobian-Aggregated Group Gradient for Efficient GRPO Training of Diffusion Models
JAGG replaces per-step gradient backpropagation in diffusion GRPO with two endpoint backward passes joined by timestep-weighted interpolation, giving ~2x backward-pass savings at modest quality cost.
-
Flow-OPD: On-Policy Distillation for Flow Matching Models
Flow-OPD applies on-policy distillation to flow matching models via specialized teachers, cold-start initialization, and manifold anchor regularization, lifting GenEval from 63 to 92 and OCR from 59 to 94 on Stable Di...
-
Sketch Then Paint: Hierarchical Reinforcement Learning for Diffusion Multi-Modal Large Language Models
Proposes HT-GRPO with sketch-then-paint staged updates, prompt-conditioned importance ratios, and hierarchical credit assignment for dMLLMs, reporting gains on GenEval and DPG plus quality metrics.
-
Power Reinforcement Post-Training of Text-to-Image Models with Super-Linear Advantage Shaping
Super-Linear Advantage Shaping (SLAS) introduces a non-linear geometric policy update for RL post-training of text-to-image models that reshapes the local policy space via advantage-dependent Fisher-Rao weighting to r...
-
Flow-OPD: On-Policy Distillation for Flow Matching Models
Flow-OPD applies on-policy distillation to flow-matching text-to-image models, lifting GenEval from 63 to 92 and OCR accuracy from 59 to 94 while preserving fidelity.
-
Flow-OPD: On-Policy Distillation for Flow Matching Models
Flow-OPD applies on-policy distillation to flow matching models, achieving GenEval of 92 and OCR accuracy of 94 on Stable Diffusion 3.5 Medium while avoiding the seesaw effect of multi-reward optimization.
-
Flow-OPD: On-Policy Distillation for Flow Matching Models
Flow-OPD applies on-policy distillation to Flow Matching models through specialized teachers, cold-start initialization, task routing, and manifold regularization, lifting GenEval from 63 to 92 and OCR from 59 to 94 o...
-
Flow-OPD: On-Policy Distillation for Flow Matching Models
Flow-OPD is a two-stage on-policy distillation method for flow matching models that lifts GenEval from 63 to 92 and OCR from 59 to 94 on SD 3.5 Medium while preserving fidelity.
-
MAR-GRPO: Stabilized GRPO for AR-diffusion Hybrid Image Generation
MAR-GRPO stabilizes GRPO for AR-diffusion hybrids via multi-trajectory expectation and uncertainty-based token selection, yielding better visual quality, stability, and spatial understanding than baselines.
-
RubricRL: Simple Generalizable Rewards for Text-to-Image Generation
Using an LLM to generate prompt-specific visual rubrics and grade each criterion independently gives a more interpretable reward that improves text-to-image model alignment beyond composite and learned scalar rewards.
-
When Models Learn to Ask Why: Adaptive Causal Reasoning for Trustworthy Medical Vision-Language Models
MedCausalX adds adaptive causal reflection tokens and trajectory-level RL on a new CRMed dataset to cut medical VLM hallucinations and raise diagnostic consistency.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.