5 papers
DRIVE: Data Curation Best Practices for Reinforcement Learning with Verifiable Reward in Competitive Code Generation
Speed Zhu, Jianwei Cai, Guang Chen +3
Recent reasoning-first models (e.g., OpenAI o1, DeepSeek R1) have spurred a resurgence of interest in RLVR. Nevertheless, advances are dominated by mathematics (e.g., AIME), with c…
ReLook: Vision-Grounded RL with a Multimodal LLM Critic for Agentic Web Coding
Yuhang Li, Chenchen Zhang, Ruilin Lv +6
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. W…
Learning to Guarantee Type Correctness in Code Generation through Type-Guided Program Synthesis
Zhechong Huang, Zhao Zhang, Ruyi Ji +6
Language models have shown remarkable proficiency in code generation; nevertheless, ensuring type correctness remains a challenge. Although traditional methods, such as constrained…
AutoCodeBench: Large Language Models are Automatic Code Benchmark Generators
Jason Chou, Ao Liu, Yuchi Deng +13
Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains, with code generation emerging as a key area of focus. While numerous benchmarks have…
ArtifactsBench: Bridging the Visual-Interactive Gap in LLM Code Generation Evaluation
Chenchen Zhang, Yuhang Li, Can Xu +17
The generative capabilities of Large Language Models (LLMs) are rapidly expanding from static code to dynamic, interactive visual artifacts. This progress is bottlenecked by a crit…