Datasets:
license: apache-2.0
task_categories:
- image-text-to-text
tags:
- mobile-gui-agent
- agent-memory
- reinforcement-learning
- conact
MemGUI-3K-Verl
RL prompt corpus of 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/). Every record is one annotated state of a MemGUI-3K trajectory in the ConAct conversation format consumed by the FARPO trainer (https://github.com/memgui-rl-anonymous/MemGUI-RL):
| file | states | trajectories | use |
|---|---|---|---|
memgui3k_train_verl.json |
73,807 | 2,661 | RL prompts (positive states only are used; folding-aware sampling controls the span:step ratio) |
memgui3k_test_verl.json |
8,296 | 295 | held-out validation (never filtered; 5,941 positive states) |
Each record has task_id, step_number, is_positive, bad_step, raw_response (the annotated
ConAct response: <thinking>, <folding>, <tool_call>, <ui_observation>, <action_intent>),
conversations (system prompt, user prompt with the folded history / UI memory / recent step record
and the screenshot, reference assistant turn) and metadata (impact / reasonableness labels of the
step, trajectory ids).
Screenshots are not duplicated here: image references have the form
MemGUI-3K/images/<task>_<attempt>_step_<k>.png and resolve against the MemGUI-3K release
(https://huggingface.co/datasets/memgui-agent-anonymous/MemGUI-3K). Set MEMGUI_IMAGE_ROOT to the
directory that contains MemGUI-3K/ when training.