EvolveCoder: Evolving Test Cases via Adversarial Verification for Code Reinforcement Learning

• URL: http://arxiv.org/abs/2603.12698v1
• Date: Fri, 13 Mar 2026 06:26:50 GMT
• Title: EvolveCoder: Evolving Test Cases via Adversarial Verification for Code Reinforcement Learning
• Abstract summary: Reinforcement learning with verifiable rewards (RLVR) is a promising approach for improving code generation in large language models.<n>We propose a solution-conditioned and adversarial verification framework that iteratively refines test cases based on the execution behaviors of candidate solutions.<n>We introduce EvolveCoder-22k, a large-scale coding reinforcement learning dataset constructed through multiple rounds of adversarial test case evolution.
• Score: 46.87136299625531
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement learning with verifiable rewards (RLVR) is a promising approach for improving code generation in large language models, but its effectiveness is limited by weak and static verification signals in existing coding RL datasets. In this paper, we propose a solution-conditioned and adversarial verification framework that iteratively refines test cases based on the execution behaviors of candidate solutions, with the goal of increasing difficulty, improving discriminative power, and reducing redundancy. Based on this framework, we introduce EvolveCoder-22k, a large-scale coding reinforcement learning dataset constructed through multiple rounds of adversarial test case evolution. Empirical analysis shows that iterative refinement substantially strengthens verification, with pass@1 decreasing from 43.80 to 31.22. Reinforcement learning on EvolveCoder-22k yields stable optimization and consistent performance gains, improving Qwen3-4B by an average of 4.2 points across four downstream benchmarks and outperforming strong 4B-scale baselines. Our results highlight the importance of adversarial, solution-conditioned verification for effective and scalable reinforcement learning in code generation.

Related papers

• Escaping Confidence Trap: Evolutionary Decoding for Mathematical Reasoning in Diffusion LLMs [69.52853771994451]
  We analyze the decoding trajectories of LLaDA 2.0 and identify a recurring diffusion confidence trap.<n>We propose Evolutionary Decoding, a training-free test-time scaling framework that views diffusion decoding as an evolutionary process.
  arXiv  Detail & Related papers  (2026-08-01T11:48:25Z)
• SCOPE: Leveraging Subgoal Critiques for Code Generation [14.869703886733761]
  We present SCOPE, a prover-d subgoal critic for code generation.<n>SCOPE adapts a Lean-oriented prover model to produce three parseable feedback fields for downstream code generation.<n>Our approach combines supervised fine-tuning, process-aligned reinforcement learning (RL), and feedback-guided inference.
  arXiv  Detail & Related papers  (2026-07-07T04:09:41Z)
• Learning from Failures: Correction-Oriented Policy Optimization with Verifiable Rewards [73.44333771806282]
  Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as an effective paradigm for improving the reasoning capabilities of large language models.<n>We propose Correction-Oriented Policy Optimization (CIPO), a simple and effective extension to RLVR that converts on-policy failed trajectories into correction-oriented supervision.<n>CIPO improves learning effectiveness while explicitly enhancing the model's ability to correct its own errors.
  arXiv  Detail & Related papers  (2026-05-14T08:22:21Z)
• ContextRL: Enhancing MLLM's Knowledge Discovery Efficiency with Context-Augmented RL [64.77036363086519]
  We propose ContextRL, a novel framework that leverages context augmentation to overcome these bottlenecks.<n>We provide the reward model with full reference solutions as context, enabling fine-grained process verification to filter out false positives.<n>We also introduce a multi-turn sampling strategy where the reward model generates mistake reports for failed attempts, guiding the policy to "recover" correct responses from previously all-negative groups.
  arXiv  Detail & Related papers  (2026-02-26T04:55:57Z)
• 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)
• Aletheia: What Makes RLVR For Code Verifiers Tick? [51.371034079170435]
  Verifiers trained via Reinforcement Learning from Verifiable Rewards (RLVR) are a prominent fixture of the Large Language Model (LLM) post-training pipeline.<n>Code verifiers remain valuable toward judging model outputs in scenarios where execution feedback is hard to obtain.<n>We examine components of the RLVR-based verifier training recipe widely credited for its success.
  arXiv  Detail & Related papers  (2026-01-17T22:30:45Z)
• LLM-based Vulnerable Code Augmentation: Generate or Refactor? [0.6588840794922407]
  Vulnerability code-bases often suffer from severe imbalance, limiting the effectiveness of Deep Learning-based vulnerability classifiers.<n>We investigate how to mitigate this by mitigating the scarcity of under-represented CWEs.<n>We find that our approaches are indeed effective in enriching vulnerable code-bases through a simple process and with reasonable quality.
  arXiv  Detail & Related papers  (2025-12-09T11:15:13Z)
• CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment [98.87395842351627]
  Large Language Models (LLMs) excel at code generation by learning from vast code corpora.<n>A fundamental semantic gap remains between their training on textual patterns and the goal of functional correctness.<n>We propose CodeRL+, a novel approach that integrates execution semantics alignment into the RLVR training pipeline for code generation.
  arXiv  Detail & Related papers  (2025-10-21T09:48:06Z)
• ReVeal: Self-Evolving Code Agents via Reliable Self-Verification [11.875519107421312]
  We introduce ReVeal, a reinforcement learning framework that evolves code generation through self-verification and tool-based evaluation.<n>At inference, this strengthened self-verification enables the model to use self-constructed tests and tool feedback to continuously evolve code for 20+ turns on LiveCodeBench despite training on only three.<n>These findings highlight the promise of ReVeal as a scalable paradigm for RL training and test-time scaling, paving the way for more robust and autonomous AI agents.
  arXiv  Detail & Related papers  (2025-06-13T03:41:04Z)
• 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)

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.