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This paper introduces HarnessEvo to decompose LLM agent harnesses into separately-evolvable slots, revealing that optimization value is localized in specific components like reflection/control, and that uniform budget-splitting is sub-optimal, advocating for targeted budget concentration.
Introduces temporal scheduling for credit allocation criteria in reinforcement learning with verifiable rewards, showing that scheduling when learning signals are applied improves policy evolution and stability.