ExploreNet: Learning Where to Explore in Diffusion GRPO

arXiv:2609.38329 · cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/stellalisy/ExploreNet

Terminology

• GRPO — 100× in this paper · explained in 6 episodes
• Diffusion Model — 23× in this paper · explained in 8 episodes
• Diffusion Models — 21× in this paper · explained in 6 episodes
• Benchmark — 15× in this paper · explained in 2 episodes
• Reinforcement Learning — 11× in this paper · explained in 4 episodes
• Alignment — 10× in this paper · explained in 1 episode
• Fine-Tuning — 9× in this paper · explained in 5 episodes
• Transformer — 9× in this paper · explained in 2 episodes
• Aggregation — 6× in this paper · explained in 1 episode
• Grounding — 5× in this paper · explained in 3 episodes
• Verifier — 5× in this paper · explained in 2 episodes
• Flow Matching — 4× in this paper · explained in 8 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Decoupling — 2× in this paper · explained in 2 episodes
• Flow Matching Model — 2× in this paper · explained in 2 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• LoRA Adapters — 2× in this paper · explained in 3 episodes
• Mode Collapse — 2× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Credit assignment — 1× in this paper · explained in 3 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Hypernetwork — 1× in this paper · explained in 3 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Personalization — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes

Sources

• Training Diffusion Models with Reinforcement Learning
• FIND: Fine-tuning Initial Noise Distribution with Policy Optimization for Diffusion Models
• Directly Fine-Tuning Diffusion Models on Differentiable Rewards
• Personalized Preference Fine-tuning of Diffusion Models
• Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
• Noise Hypernetworks: Amortizing Test-Time Compute in Diffusion Models
• DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models
• GenEval: An Object-Focused Framework for Evaluating Text-to-Image Alignment
• TempFlow-GRPO: When Timing Matters for GRPO in Flow Models
• ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment
• T2I-CompBench++: An Enhanced and Comprehensive Benchmark for Compositional Text-to-image Generation
• T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoT
• GenEval 2: Addressing Benchmark Drift in Text-to-Image Evaluation
• Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation
• Spatially Adaptive Noise Injection
• Aligning Text-to-Image Models using Human Feedback
• MixGRPO: Unlocking Flow-based GRPO Efficiency with Mixed ODE-SDE
• Verifiable Visual Rewards Transfer from Synthetic Scenes to Natural Prompts
• BranchGRPO: Stable and Efficient GRPO with Structured Branching in Diffusion Models
• Learning Sampling Parameters for Diffusion Models

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