Huifeng Sun

Yunhui Xia, Wei Shen, Yan Wang et al.

We introduce LeetCodeDataset, a high-quality benchmark for evaluating and training code-generation models, addressing two key challenges in LLM research: the lack of reasoning-focused coding benchmarks and self-contained training testbeds. By curating LeetCode Python problems with rich metadata, broad coverage, 100+ test cases per problem, and temporal splits (pre/post July 2024), our dataset enables contamination-free evaluation and efficient supervised fine-tuning (SFT). Experiments show reasoning models significantly outperform non-reasoning counterparts, while SFT with only 2.6K model-generated solutions achieves performance comparable to 110K-sample counterparts. The dataset and evaluation framework are available on Hugging Face and Github.

Zhechong Huang, Zhao Zhang, Zeyu Sun et al.

The specification synthesis task aims to automatically generate specifications, together with any necessary auxiliary verification annotations, for existing programs. This task is important because such specifications serve as behavioral contracts that support modular reasoning and reusable verification across a codebase. At the same time, it remains challenging because verifier-only feedback is fundamentally incomplete: passing verification establishes soundness, but cannot distinguish weak specifications from strong ones. What is missing is a fine-grained signal for specification completeness. We present SpecRL, a reinforcement learning framework for specification synthesis in Dafny. SpecRL introduces a self-contained pipeline that generates negative tests, i.e., input-output pairs that can never be produced by the program. We use the fraction of these negative tests rejected by a candidate specification as a signal of specification completeness, which is integrated into the reward for RL training. Experiments across four model sizes show that SpecRL improves both specification strength and verification success over SFT and RL with a binary specification-strength reward, generalizes to an out-of-distribution benchmark, and remains competitive on that unseen benchmark compared to much larger general-purpose LLMs.