AIBullisharXiv – CS AI · 5h ago6/10
🧠
SCALE: Scalable Cross-Attention Learning with Extrapolation for Agentic Workflow Scheduling
Researchers introduce SCALE, a deep reinforcement learning scheduler that enables LLM-based agentic systems to generalize across different cluster sizes without retraining. Using cross-attention architecture and a novel regularization technique, the system achieves 8.9% improvement in response times when scaled from 16 to 48 nodes, addressing a critical infrastructure challenge for distributed AI workloads.