Amarsaikhan Batjargal, Noriko Tomuro · 2026 · arXiv
DOI: 10.48550/arXiv.2610.26416 · open-access PDF
Reinforcement Learning with Verifiable Rewards (RLVR) sangat cocok untuk menghasilkan kode karena program yang dihasilkan dapat dinilai secara obyektif melalui eksekusi. Execution juga menyediakan informasi perilaku yang dapat digunakan kembali sebagai pengawasan tambahan. The Synergistic Policy and Reward Co-Evolving Framework (SPARK), awalnya diusulkan untuk tugas-tugas penilaian, memanfaatkan gagasan ini dengan memulihkan peluncuran pelatihan dan hasil evaluasi mereka untuk melatih sebuah model tidak hanya untuk menghasilkan solusi tetapi juga untuk mengevaluasi dan meningkatkan mereka.
Machine translation (Meta M2M100); the English original follows.
Reinforcement learning with verifiable rewards (RLVR) is particularly well suited to code generation because generated programs can be objectively evaluated through execution. Execution also provides behavioral information that can be reused as additional supervision. The Synergistic Policy and Reward Co-Evolving Framework (SPARK), originally proposed for reasoning tasks, exploits this idea by recycling training rollouts and their evaluation results to train a model not only to generate solutions but also to evaluate and improve them. We investigate whether this policy-reward co-evolution appr
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