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A technical exploration of stateless actors in Swift, discussing their uses, trade-offs, and comparisons with structs using concurrent functions, including examples like network clients and background actors.
This paper studies when end-to-end reinforcement learning training improves multi-agent LLM workflows, comparing shared-policy and isolated-policy training across different workflows, tasks, and model scales, revealing conditional tradeoffs.
A developer tested adding 'think step by step' to a customer support AI agent, achieving a 3% accuracy gain but with a 40% latency increase and doubled costs, concluding that the net impact was negative and highlighting the importance of measuring production tradeoffs.