5 papers
Combinatorial Synthesis: Scaling Code RLVR via Atomic Decomposition and Recombination
Jiasheng Zheng, Boxi Cao, Boxi Yu +6
Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as the cornerstone for shaping the remarkable coding abilities of Large Language Models (LLMs). However,…
FeedbackEval: A Benchmark for Evaluating Large Language Models in Feedback-Driven Code Repair Tasks
Dekun Dai, MingWei Liu, Anji Li +5
Code repair is a fundamental task in software development, facilitating efficient bug resolution and software maintenance. Although large language models (LLMs) have demonstrated c…
From What to How: Bridging User Requirements with Software Development Using Large Language Models
Xiao He, Ru Chen, Jialun Cao
Recently, large language models (LLMs) are extensively utilized to enhance development efficiency, leading to numerous benchmarks for evaluating their performance. However, these b…
Towards Scalable Training for Handwritten Mathematical Expression Recognition
Haoyang Li, Jiaqing Li, Jialun Cao +2
Large foundation models have achieved significant performance gains through scalable training on massive datasets. However, the field of \textbf{H}andwritten \textbf{M}athematical…
Doc2Feat-Bench: Evaluating Documentation-Driven Feature Addition
Le Deng, Zhonghao Jiang, Jialun Cao +2
Documentation changes in mature software projects often describe newly introduced or modified behavior. This makes them a natural basis for documentation-driven feature addition, w…