MIST-RL: Mutation-based Incremental Suite Testing via Reinforcement Learning
Large Language Models (LLMs) often fail to generate correct code on the first attempt, which requires using generated unit tests as verifiers to validate the solutions. Despite the success of recent verification methods, they remain constrained by a "scaling-by-quantity" paradigm. This brute-force approach suffers from a critical limitation: it yields diminishing returns in fault detection while causing severe test redundancy. To address this, we propose MIST-RL (Mutation-based Incremental Suite Testing via Reinforcement Learning), a framework that shifts the focus to "scaling-by-utility". We formulate test generation as a sequential decision process optimized via Group Relative Policy Op...
| Authors | Sicheng Zhu, Jiajun Wang, Jiawei Ai et al. |
|---|---|
| Primary category | cs.AI |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
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