Pengzhi Gao

Recent advances in mobile Graphical User Interface (GUI) agents highlight the growing need for comprehensive evaluation benchmarks. While new online benchmarks offer more realistic testing than offline ones, they tend to focus on the agents’ task instruction-following ability while neglecting the...

Reinforcement learning with verifiable rewards (RLVR) has emerged as a prominent paradigm for enhancing the reasoning capabilities of large language models (LLMs). However, the entropy of LLMs usually collapses during RLVR training, leading to premature convergence to suboptimal local minima and...

Multi-turn interaction remains challenging for online reinforcement learning. Current GRPO-based methods—either at the trajectory level or the step level—still suffer from fundamental challenges in multi-turn settings: they allocate sampling uniformly across tasks regardless of difficulty, propag...