Beyond pass@k: Redundancy-Aware RLVR for Multi-Sample Code Generation
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914607087484928 |
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| author | Florian, Le Bronnec Verine, Alexandre Yokota, Rio Negrevergne, Benjamin |
| author_facet | Florian, Le Bronnec Verine, Alexandre Yokota, Rio Negrevergne, Benjamin |
| contents | LLMs for code generation are commonly evaluated in repeated-sampling settings using Pass@k, where multiple candidate programs are executed against unit tests under a finite sampling budget. While recent verifier-based reinforcement learning (RLVR) methods improve executable correctness, how these objectives affect redundancy among sampled programs remains poorly understood. In this work, we study implementation-level redundancy in code generation using JPlag, a plagiarism-detection system for code. Across models and benchmarks, we show that correctness-only RLVR often concentrates generations around repeated implementations, whereas Pass@k-aware objectives maintain lower redundancy and improve larger-budget performance. Motivated by these observations, we augment RLVR with direct anti-redundancy rewards based on JPlag similarity. Across 3 models and 3 benchmarks, discouraging near-duplicate generations reliably improves finite-budget executable performance, often matching or outperforming specialized Pass@k-aware objectives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_28022 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Beyond pass@k: Redundancy-Aware RLVR for Multi-Sample Code Generation Florian, Le Bronnec Verine, Alexandre Yokota, Rio Negrevergne, Benjamin Computation and Language Software Engineering LLMs for code generation are commonly evaluated in repeated-sampling settings using Pass@k, where multiple candidate programs are executed against unit tests under a finite sampling budget. While recent verifier-based reinforcement learning (RLVR) methods improve executable correctness, how these objectives affect redundancy among sampled programs remains poorly understood. In this work, we study implementation-level redundancy in code generation using JPlag, a plagiarism-detection system for code. Across models and benchmarks, we show that correctness-only RLVR often concentrates generations around repeated implementations, whereas Pass@k-aware objectives maintain lower redundancy and improve larger-budget performance. Motivated by these observations, we augment RLVR with direct anti-redundancy rewards based on JPlag similarity. Across 3 models and 3 benchmarks, discouraging near-duplicate generations reliably improves finite-budget executable performance, often matching or outperforming specialized Pass@k-aware objectives. |
| title | Beyond pass@k: Redundancy-Aware RLVR for Multi-Sample Code Generation |
| topic | Computation and Language Software Engineering |
| url | https://arxiv.org/abs/2605.28022 |