In standard GRPO, tokens whose importance ratios fall outside the clip range receive zero gradient; CISPO instead detaches the clipped weights and uses them as scaling coefficients on the log-probability gradient, ensuring all tokens contribute to learning, including rare but critical tokens such as pruning decisions and query reformulations. Advantages are computed via within-group normalization, where each query's 8 rollouts compete and only their relative rewards determine the gradient.
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，这一点在OpenClaw龙虾下载中也有详细论述
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Operates with llama.cpp, Ryzen AI SW, FastFlowLM, and additional platforms.
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