Create README.md

README.md

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+---
+language:
+- en
+base_model:
+- mair-lab/sft-simple
+---
+
+# 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`](https://huggingface.co/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](https://arxiv.org/abs/2508.01119)
+
+## 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](https://github.com/saba96/EARL?tab=readme-ov-file)
+
+## Full Benchmark Results
+
+| Model                      | Base Model | OmniEdit | EmuEdit | AURORA | MB   | VisMin | I2EBench | **AVG** |
+|---------------------------|------------|----------|---------|--------|------|--------|----------|---------|
+| Magicbrush                | SD v1.5    | 3.43     | 3.28    | 3.01   | 3.64 | 3.48   | 3.06     | 3.32    |
+| InstructPix2Pix           | SD v1.5    | 3.97     | 3.24    | 3.05   | 3.12 | 2.94   | 3.23     | 3.26    |
+| Aurora                    | SD v1.5    | 4.50     | 4.40    | 4.12   | 4.62 | 3.82   | 3.58     | 4.17    |
+| Omnigen*                  | -          | 5.68     | 5.00    | 4.10   | 4.68 | 4.09   | 4.68     | 4.70    |
+| **SFT (S)**               | Emu3       | 5.73     | 3.66    | 3.58   | 3.19 | 3.57   | 3.59     | 3.88    |
+| **EARL SFT (S) → RL (S+C)** | SFT (S)    | **6.39** | 4.47    | **4.27** | 4.52 | 4.93   | 4.19     | **4.80** |
+
+> 🚀 **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