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ARXIV:2603.12698 · REINFORCEMENT LEARNING FOR CODE GENERATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.12698REINFORCEMENT LEARNING FOR CODE GENERATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
EvolveCoder enhances code generation through adversarial verification and a refined RL dataset.
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Pain EvolveCoder enhances code generation through adversarial verification and a refined RL dataset.
Evidence 0 refs | 0 sources | 17% coverage
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EvolveCoder enhances code generation through adversarial verification and a refined RL dataset. In this paper, we propose a solution-conditioned and adversarial verification framework that iteratively refines test cases based on the execution behaviors of…
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.…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Empirical analysis shows that iterative refinement substantially strengthens verification, with pass@1 decreasing from 43.80 to 31.22.
Reinforcement Learning for Code Generation moved forward this cycle; last verified April 2026. Public score 7.0/10.
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EvolveCoder enhances code generation through adversarial verification and a refined RL dataset.
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10.48550/arXiv.2603.12698EvolveCoder enhances code generation through adversarial verification and a refined RL dataset.
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.
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PROBLEM
EvolveCoder enhances code generation through adversarial verification and a refined RL dataset. 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, wi...
METHOD
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-con...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Empirical analysis shows that iterative refinement substantially strengthens verification, with pass@1 decreasing from 43.80 to 31.22.
WHY NOW
Reinforcement Learning for Code Generation moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
EvolveCoder enhances code generation through adversarial verification and a refined RL dataset. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Empirical analysis shows that iterative refinement substantially strengthens verification, with pass@1 decreasing from 43.80 to 31.22.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Reinforcement Learning for Code Generation moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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EvolveCoder enhances code generation through adversarial verification and a refined RL dataset.
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Reinforcement Learning for Code Generation
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