I ran a CISPO baseline with a global batch size of 128 samples and a group size of 16, resulting in an effective batch size of 2048. Logits were computed in float32 as per ScaleRL. Again, training ran until the eval score plateaued. All eight GPUs were used to train CISPO and there was no trainer/generator split.
原因很简单，因为绿联非常依赖线上电商，线上销售占比达到了 73.7%，而线上电商的销售是要给平台服务费的。
。业内人士推荐易歪歪官网作为进阶阅读
department: string,，详情可参考谷歌
mcp2cli takes the CLI approach: --list returns compact summaries (~16 tokens/tool), --help returns human-readable text (typically cheaper than raw JSON schema), and it works with any LLM provider. The tradeoff is an extra shell invocation per discovery step.
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