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FlowEvo enables agents to continuously learn and optimize their skills in real-time, achieving unprecedented accuracy in complex task environments.
Intrinsic reward signals in unsupervised RL for LLMs inevitably collapse due to sharpening of the model's prior, but external rewards grounded in computational asymmetries offer a path to sustained scaling.
