Chenchen Zhang

Translating natural language mathematical statements into formal, executable code is a fundamental challenge in automated theorem proving. While prior work has focused on generation and compilation success, little attention has been paid to the critic phase—the evaluation of whether generated for...

Recent progress in large language models (LLMs) is largely driven by scaling training compute through either pre-training with next-token prediction (NTP) or post-training with reinforcement learning (RL). The former contributes to learning broad knowledge and skills from general data, while stru...

While Large Language Models (LLMs) excel at algorithmic code generation, they struggle with front-end development, where correctness is judged on rendered pixels and interaction. We present ReLook, an agentic, vision-grounded reinforcement learning framework that empowers an agent to close a robu...

Parallel thinking offers a promising avenue for scaling test-time compute in Large Language Models (LLMs), enabling them to explore diverse solution paths simultaneously before aggregating them into a final answer. However, coordinating the exploration and aggregation stages remains challenging,...