FrontierSmith: Synthesizing Open-Ended Coding Problems at Scale
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Computer Science > Machine Learning
Title:FrontierSmith: Synthesizing Open-Ended Coding Problems at Scale
Abstract: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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.14445 [cs.LG] |
| (or arXiv:2605.14445v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.14445
arXiv-issued DOI via DataCite (pending registration)
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