| --- |
| license: apache-2.0 |
| base_model: memgui-agent-anonymous/MemGUI-8B-SFT |
| pipeline_tag: image-text-to-text |
| tags: [mobile-gui-agent, agent-memory, reinforcement-learning, qwen3-vl, conact] |
| --- |
| |
| # MemGUI-8B-RL |
|
|
| Anonymous release for the ICLR 2027 submission *FARPO: Folding-Aware Reward-Decoupled Policy Optimization for Long-Horizon Mobile GUI Agents* (project MemGUI-RL). Project page: https://memgui-rl-anonymous.github.io/ |
|
|
| MemGUI-8B-RL is MemGUI-8B-SFT (Qwen3-VL-8B-Instruct supervised on MemGUI-3K) post-trained for 100 |
| optimizer steps with **FARPO** (Folding-Aware Reward-Decoupled Policy Optimization, span-to-step ratio |
| rho = 9). The policy speaks the ConAct (Context-as-Action) interface of MemGUI-Agent: every response |
| contains a folding directive for its own history, an optional memory operation and the next GUI action. |
|
|
| | benchmark | MemGUI-8B-SFT | **MemGUI-8B-RL** | |
| | --- | --- | --- | |
| | MemGUI-Bench Pass@1 / Pass@3 / IRR (128 tasks) | 23.4 / 35.9 / 30.2 | **30.5 / 48.4 / 39.0** | |
| | MobileWorld GUI-only success rate (117 tasks, OOD) | 17.9 | **19.7** | |
|
|
| ## Usage |
|
|
| The checkpoint is a standard `Qwen3VLForConditionalGeneration` model (weights in bf16, ~17.5 GB). |
| Use it with the ConAct system prompt and the MemGUI-Agent runtime unchanged: |
|
|
| ```python |
| from transformers import AutoProcessor, Qwen3VLForConditionalGeneration |
| model = Qwen3VLForConditionalGeneration.from_pretrained("memgui-rl/MemGUI-8B-RL", torch_dtype="bfloat16", device_map="auto") |
| processor = AutoProcessor.from_pretrained("memgui-rl/MemGUI-8B-RL") |
| ``` |
|
|
| Training code and launch scripts: https://github.com/memgui-rl-anonymous/MemGUI-RL |
| Training data: https://huggingface.co/datasets/memgui-rl/MemGUI-3K-Verl |
| Evaluation logs: https://huggingface.co/datasets/memgui-rl/MemGUI-RL-Eval |
|
|
