FrontierSmith: Synthesizing Open-Ended Coding Problems at Scale
Authors: name: Runyuan He, name: Qiuyang Mang, name: Shang Zhou, name: Kaiyuan Liu, name: Hanchen Li, name: Huanzhi Mao, name: Qizheng Zhang, name: Zerui Li, name: Bo Peng, name: Lufeng Cheng, name: Tianfu Fu, name: Yichuan Wang, name: Wenhao Chai, name: Jingbo Shang, name: Alex Dimakis, name: Joseph E. Gonzalez, name: Alvin Cheung

Many real-world coding challenges are open-ended and admit no known optimal solution. Yet, recent progress in LLM coding has focused on well-defined tasks such as feature implementation, bug fixing, and competitive programming. Open-ended coding remains a weak spot for LLMs, largely because open-ended training problems are scarce and expensive to construct. Our goal is to synthesize open-ended coding problems at scale to train stronger LLM coders. We introduce FrontierSmith, an automated system for iteratively evolving open-ended problems from existing closed-ended coding tasks. Starting from competitive programming problems, FrontierSmith generates candidate open-ended variants by changing the problems'goals, restricting outputs, and generalizing inputs. It then uses a quantitative idea divergence metric to select problems that elicit genuinely diverse approaches from different solvers. Agents then generate test cases and verifiers for the surviving candidates. On two open-ended coding benchmarks, training on our synthesized data yields substantial gains over the base models: Qwen3.5-9B improves by +8.82 score on FrontierCS and +306.36 (Elo-rating-based performance) on ALE-bench; Qwen3.5-27B improves by +12.12 and +309.12, respectively. The synthesized problems also make agents take more turns and use more tokens, similar to human-curated ones, suggesting that closed-ended seeds can be a practical starting point for long-horizon coding data.