arXiv:2606.06820v1 Announce Type: new Abstract: Agentic Large Language Model (LLM) systems decompose complex tasks into workflow Directed Acyclic Graphs (DAGs) whose primitives must be scheduled on heterogeneous clusters. Existing deep reinforcement learning (DRL) schedulers are tied to a fixed cluster size and require retraining whenever the number of servers changes. We propose SCALE (Scalable Cross-Attention Learning with Extrapolation), a DRL scheduler that generalizes to unseen cluster scales without fine-tuning. SCALE employs a cross-attention pointer network where task features query ag
The increasing complexity of agentic AI workflows and the demand for efficient resource allocation accelerate the need for scalable scheduling solutions for heterogeneous compute infrastructure.
This development addresses a key limitation in deploying advanced agentic AI systems by enabling efficient resource management across varying compute infrastructures, fostering broader adoption and performance gains.
AI workflow schedulers can now adapt to dynamic cluster sizes without continuous retraining, reducing operational overhead and improving the scalability of agentic LLM deployments.
- · AI compute infrastructure providers
- · Developers of agentic LLM systems
- · Cloud service providers
- · Organizations deploying large-scale AI
- · Companies with inflexible AI scheduling solutions
- · Hardware vendors without scalable resource management
Improved efficiency and reduced operational costs for deploying agentic AI workflows on large compute clusters.
Accelerated development and adoption of more complex and sophisticated AI agent systems due to reliable scheduling.
Increased demand for heterogeneous compute infrastructure optimized for dynamic AI workload management.
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Read at arXiv cs.LG