Published on October 23, 2025, the work presents BAPO, a method using balanced policy optimization and adaptive clipping for off-policy training scenarios such as partial rollout and experience reuse.
Published on October 23, 2025, the work presents BAPO, a method for stabilizing off-policy reinforcement learning in language models. The proposal combines balanced policy optimization with adaptive clipping and addresses scenarios such as partial rollout and experience reuse. The material does not provide metrics or quantitative results.
Engineers researching off-policy training can consult the paper and its code to evaluate the method in these scenarios; check the original material for implementation details and test results. If using AI to study or apply the proposal, avoid entering identifiable organizational data and check the privacy and retention policies of the tool. BAPO is a research proposal, not a Rota Nacional capability.