group-relative-policy-optimization

SAGA framework uses frozen multimodal large language models to provide attribute-aware supervision for vision encoders via Group Relative Policy Optimization, improving zero-shot image retrieval by 3–6 points on fine-grained benchmarks.

The S2L-PO framework uses smaller models as natural explorers to enhance policy diversity in GRPO for training large language models. It achieves faster convergence and improves accuracy on mathematical reasoning benchmarks while reducing rollout compute.