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Qwen-AgentWorld achieves unprecedented simulation fidelity, outperforming existing models and enabling scalable agentic reinforcement learning across diverse real-world environments.
Adapter design can make or break coding performance in OpenClaw-style agents, with a full adapter boosting success rates by over 50 percentage points.
Don't let valuable steps in failed trajectories go unnoticed: GraphGPO leverages state-transition graphs for fine-grained credit assignment in agentic RL, boosting performance and efficiency.
Cycle-consistent learning unlocks self-improvement in vision-language models, enabling them to reason about their own generations and boosting performance across understanding and generation tasks.
A new family of GUI agents, GUI-Owl-1.5, leapfrogs existing open-source models on 20+ GUI benchmarks, proving that multi-platform, real-time GUI automation is now within reach.
