HardTests: Synthesizing High-Quality Test Cases for LLM Coding
Authors: name: Zhongmou He, name: Yee Man Choi, name: Kexun Zhang, name: Jiabao Ji, name: Junting Zhou, name: Dejia Xu, name: Ivan Bercovich, name: Aidan Zhang, name: Lei Li

Verifiers play a crucial role in large language model (LLM) reasoning, needed
by post-training techniques such as reinforcement learning. However, reliable
verifiers are hard to get for difficult coding problems, because a
well-disguised wrong solution may only be detected by carefully human-written
edge cases that are difficult to synthesize. To address this issue, we propose
HARDTESTGEN, a pipeline for high-quality test synthesis using LLMs. With this
pipeline, we curate a comprehensive competitive programming dataset HARDTESTS
with 47k problems and synthetic high-quality tests. Compared with existing
tests, HARDTESTGEN tests demonstrate precision that is 11.3 percentage points
higher and recall that is 17.5 percentage points higher when evaluating
LLM-generated code. For harder problems, the improvement in precision can be as
large as 40 points. HARDTESTS also proves to be more effective for model
training, measured by downstream code generation performance. We will
open-source our dataset and synthesis pipeline at
https://leililab.github.io/HardTests/.