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Amarsaikhan Batjargal

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Preprint Oct 2026

Co-Evolving Policy and Reward for Code Generation: A Mechanistic Study of Single-Adapter Drift

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 Frame...

Amarsaikhan Batjargal, Noriko Tomuro · 0 citations

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