ARC Learns Dynamic Agent Configurations
ARC introduces a reinforcement learning policy to dynamically configure LLM-based agent systems per query, selecting optimal workflows, tools, and prompts. It outperforms fixed templates on reasoning and tool-augmented QA benchmarks. The approach boosts accuracy by up to 25% while cutting token and runtime costs.








