A new arXiv paper, Co-Evolving Policy and Reward for Code Generation: A Mechanistic Study of Single-Adapter Drift, investigates a specific failure mode in reinforcement learning for code generation. The abstract argues that reinforcement learning with verifiable rewards (RLVR) is particularly well suited to this domain, since generated programs can be evaluated objectively by executing them.
The authors note that execution does more than provide a pass/fail signal: it also yields behavioral information that can be used during training. The paper's focus is on single-adapter drift, a phenomenon that arises when the policy and reward are updated together in a co-evolving setup.
Because the abstract is truncated, the full findings are not available in the source. The study appears to target a mechanistic understanding of this drift, rather than proposing a new algorithm outright. Readers interested in the details will need to consult the full paper on arXiv.