Player-1: Learning to Play from Synthetic Experience
cs.CV, cs.AI, cs.HC
Submitted: 2026-10-14
Updated: 2026-10-14
Comments: Project Page: https://player-1-project.github.io/ and code: https://github.com/TencentARC/Player-1
Code: https://github.com/TencentARC/Player-1
Project page: https://player-1-project.github.io
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- Qwen3-VL Technical Report
- Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos
- GameDevBench: Evaluating Agentic Capabilities Through Game Development
- D2E: Scaling Vision-Action Pretraining on Desktop Data for Transfer to Embodied AI
- Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence
- GameWAM: A World Action Model for Video Games
- The Dawn of GUI Agent: A Preliminary Case Study with Claude 3.5 Computer Use
- SPADE: Self-Play in Adaptive Synthetic Executable Environments
- Qwen-CUA: Native Computer Use for (almost) Everything
- AI for Games in the Foundation Model Era
- GameCraft-Bench: Can Agents Build Playable Games End-to-End in a Real Game Engine?
- GPT-4o System Card
- GameWorld: Towards Standardized and Verifiable Evaluation of Multimodal Game Agents
- UI-TARS: Pioneering Automated GUI Interaction with Native Agents
- Odysseus: Scaling VLMs to 100+ Turn Decision-Making in Games via Reinforcement Learning
- Scaling Instructable Agents Across Many Simulated Worlds
- SIMA 2: A Generalist Embodied Agent for Virtual Worlds
- Lumine: An Open Recipe for Building Generalist Agents in 3D Open Worlds
- CUA-Gym: Scaling Verifiable Training Environments and Tasks for Computer-Use Agents
- UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning
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