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Multi-agent LLM training falters when updates are decoupled from joint state transitions; grouping interacting agent outputs into cardinality-normalized set actions solves credit assignment across both static and dynamically routed systems.
Aligning LLM reasoning with a dedicated recommendation head via reinforcement learning yields state-of-the-art recommendation performance in real-world systems.
