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Sharing the right LoRA factor can significantly enhance fine-tuning performance in federated learning, with a novel adaptive strategy that outperforms traditional methods.
Agentic-LTPO boosts long-term performance by 57.2% in adaptive physical layer systems, revolutionizing how we approach real-time network optimization.
Stop wasting compute: a learned policy can intelligently allocate LLM inference budgets, boosting accuracy by up to 12.8% compared to uniform allocation.
