MIST-RL: Mutation-based Incremental Suite Testing via Reinforcement Learning

• URL: http://arxiv.org/abs/2603.01409v1
• Date: Mon, 02 Mar 2026 03:22:44 GMT
• Title: MIST-RL: Mutation-based Incremental Suite Testing via Reinforcement Learning
• Authors: Sicheng Zhu, Jiajun Wang, Jiawei Ai, Xin Li,
• Abstract summary: MIST-RL (Mutation-based Incremental Suite Testing via Reinforcement Learning) is a framework that shifts the focus to "scaling-by-utility"<n>We introduce a novel incremental mutation reward combined with dynamic penalties, which incentivizes the model to discover new faults while it suppresses functionally equivalent assertions.<n>Experiments on HumanEval+ and MBPP+ demonstrate that MIST-RL outperforms state-of-the-art baselines.
• Score: 19.054149750597933
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 Optimization (GRPO). Specifically, we introduce a novel incremental mutation reward combined with dynamic penalties, which incentivizes the model to discover new faults while it suppresses functionally equivalent assertions. Experiments on HumanEval+ and MBPP+ demonstrate that MIST-RL outperforms state-of-the-art baselines. It achieves a +28.5% higher mutation score while reducing the number of test cases by 19.3%. Furthermore, we show that these compact, high-utility tests serve as superior verifiers, which improves downstream code reranking accuracy on HumanEval+ by 3.05% over the SOTA baseline with 10 candidate samples. The source code and data are provided in the supplementary material.

Related papers

• CVeDRL: An Efficient Code Verifier via Difficulty-aware Reinforcement Learning [57.24524263804788]
  Code verifiers play a critical role in post-verification for LLM-based code generation.<n>Existing supervised fine-tuning methods suffer from data scarcity, high failure rates, and poor inference efficiency.<n>We show that naive RL with only functionality rewards fails to generate effective unit tests for difficult branches and samples.
  arXiv  Detail & Related papers  (2026-01-30T10:33:29Z)
• LaSeR: Reinforcement Learning with Last-Token Self-Rewarding [54.72617309922891]
  Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a core paradigm for enhancing the reasoning capabilities of Large Language Models (LLMs)<n>Previous practice requires the LLM to sequentially generate solutions and self-verifications using two separate prompt templates, which significantly reduces efficiency.<n>We propose LaSeR (Reinforcement Learning with Last-Token Self-Rewarding), an algorithm that simply augments the original RLVR loss with a MSE loss.
  arXiv  Detail & Related papers  (2025-10-16T17:55:11Z)
• ATGen: Adversarial Reinforcement Learning for Test Case Generation [78.48498301767079]
  Large Language Models (LLMs) excel at code generation, yet their outputs often contain subtle bugs.<n>Existing test generation methods rely on static datasets.<n>We introduce ATGen, a framework that trains a test case generator via adversarial reinforcement learning.
  arXiv  Detail & Related papers  (2025-10-16T12:49:25Z)
• Verification Limits Code LLM Training [23.67882363039948]
  Large language models for code generation increasingly rely on synthetic data, where both problem solutions and verification tests are generated by models.<n>In this work, we study how verification design and strategies influence model performance.
  arXiv  Detail & Related papers  (2025-09-25T07:23:30Z)
• TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning [11.573904453859098]
  Reinforcement Learning (RL) has become a powerful tool for enhancing the reasoning abilities of large language models (LLMs)<n>Yet, RL's success relies on the reliability of rewards, which are provided by verifiers.<n>In this paper, we expose and analyze a widespread problem--false negatives--where verifiers wrongly reject correct model outputs.<n>We propose tinyV, a lightweight LLM-based verifier that augments existing rule-based methods.
  arXiv  Detail & Related papers  (2025-05-20T17:16:44Z)
• PRIMG : Efficient LLM-driven Test Generation Using Mutant Prioritization [0.0]
  PRIMG (Prioritization and Refinement Integrated Mutation-driven Generation) is a novel framework for incremental and adaptive test case generation for Solidity smart contracts.<n> PRIMG integrates a mutation prioritization module, which employs a machine learning model trained on mutant subsumption graphs to predict the usefulness of surviving mutants.<n>The prioritization module consistently outperformed random mutant selection, enabling the generation of high-impact tests with reduced computational effort.
  arXiv  Detail & Related papers  (2025-05-08T18:30:22Z)
• Learning to Solve and Verify: A Self-Play Framework for Code and Test Generation [69.62857948698436]
  Recent advances in large language models (LLMs) have improved their performance on coding benchmarks.<n>However, improvement is plateauing due to the exhaustion of readily available high-quality data.<n>We propose Sol-Ver, a self-play solver-verifier framework that jointly improves a single model's code and test generation capacity.
  arXiv  Detail & Related papers  (2025-02-20T18:32:19Z)
• Scaling Test-Time Compute Without Verification or RL is Suboptimal [70.28430200655919]
  We show that finetuning LLMs with verifier-based (VB) methods based on RL or search is far superior to verifier-free (VF) approaches based on distilling or cloning search traces, given a fixed amount of compute/data budget.<n>We corroborate our theory empirically on both didactic and math reasoning problems with 3/8B-sized pre-trained LLMs, where we find verification is crucial for scaling test-time compute.
  arXiv  Detail & Related papers  (2025-02-17T18:43:24Z)
• Scalable Similarity-Aware Test Suite Minimization with Reinforcement Learning [6.9290255098776425]
  TripRL is a novel technique to produce a diverse reduced test suite with high test effectiveness.<n>We show that TripRL's runtime scales linearly with the magnitude of the Multi-Criteria Test Suite Minimization problem.
  arXiv  Detail & Related papers  (2024-08-24T08:43:03Z)
• Noisy Adaptive Group Testing using Bayesian Sequential Experimental Design [63.48989885374238]
  When the infection prevalence of a disease is low, Dorfman showed 80 years ago that testing groups of people can prove more efficient than testing people individually. Our goal in this paper is to propose new group testing algorithms that can operate in a noisy setting.
  arXiv  Detail & Related papers  (2020-04-26T23:41:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.