Program Semantic Inequivalence Game with Large Language Models
Quick summary
arXiv:2505.03818v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) can achieve strong performance on everyday coding tasks, but they can fail on complex tasks that require non-trivial reasoning about program semantics. Finding training examples to teach LLMs to solve these tasks can be challenging. In this work, we explore a method to synthetically generate code reasoning training data based on a semantic inequivalence game (SInQ): a generator agent creates program variants that are semantically distinct, derived from a dataset of real-world programming tasks, while an eval
Key takeaways
- arXiv:2505.03818v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) can achieve strong performance on everyday coding tasks, but they can fail on complex tasks that require non-trivial reasoning about program semantics.
- Finding training examples to teach LLMs to solve these tasks can be challenging.
- In this work, we explore a method to synthetically generate code reasoning training data based on a semantic inequivalence game (SInQ): a generator agent creates program variants that are semantically distinct, derived from a dataset of real-world programming tasks, while an eval
Why it matters
“Program Semantic Inequivalence Game with Large Language Models” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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