sft-simple.rl-simple-n-complex

EARL - RL Fine-tuned (S + C) (8B)

Model Name: mair-lab/sft-simple.rl-simple-n-complex
Model Size: 8B parameters
Base Checkpoint: mair-lab/sft-simple
Training Method: Supervised Fine-Tuning (SFT) on Simple Edits → Reinforcement Learning (RL) on Simple + Complex Edits
Datasets: Simple Edit (S), Complex Edit (C)

This model is part of the EARL benchmark study:
📄 EARL: The Promise of RL for Autoregressive Image Editing

Model Summary

This RL fine-tuned model builds on the SFT-simple checkpoint, using reinforcement learning to improve performance on both simple and complex edit tasks. It’s optimized using a human-aligned reward function across diverse editing instructions.

➡️ Inference instructions: GitHub Repo

Full Benchmark Results

🚀 Highlight: Our RL model outperforms all supervised and diffusion baselines, setting a new state-of-the-art across the EARL benchmark with 4.80 AVG.

Use Cases

• Simple edits of object, attribute, style and environment changes
• Complex edits of counting, spatial relation and action changes
• Instruction-following visual transformations