AGPO applies asymmetric negative-dominant reinforcement and variance-scaled group advantages in RLVR to preserve base model exploration while boosting accuracy and pass@k on math benchmarks and industrial ad relevance data.
For k≥5 , we need to check if it’s possible to ensure that for alln≥15 , there exist two distinct elements in each subset whose sum is n
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AGPO: Asymmetric Group Policy Optimization for Verifiable Reasoning and Search Ads Relevance at JD
AGPO applies asymmetric negative-dominant reinforcement and variance-scaled group advantages in RLVR to preserve base model exploration while boosting accuracy and pass@k on math benchmarks and industrial ad relevance data.