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MetaRAG achieves a superior accuracy-efficiency trade-off in agentic RAG by aligning decision-making with the model's internal beliefs, outperforming traditional RL methods.
On-policy self-distillation can boost diffusion model performance by up to 44% while slashing training time by over 60%.
Coordination mechanisms can significantly enhance the robustness of dual-action policies, but some methods may inadvertently increase false alarms.