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Rollouts are filtered by recall quality. Trajectories with high recall (above 50% trajectory recall and 40% output recall) are retained in full. Those with lower recall are included at a diminishing rate. A small fraction (up to 5%) of zero-recall trajectories are included as negative examples, deduplicated by query, to expose the model to failure modes, long rollouts, and potentially valid abstentions without letting them dominate the training signal. Trajectories where the model explored well but concluded poorly (where trajectory recall substantially exceeds output recall) are excluded entirely, as training on them would reinforce the disconnect between exploration and selection. When multiple rollouts for the same query achieve high output recall, only one is kept to prevent overrepresentation of easy queries. Malformed outputs are discarded.

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百度贴吧官方数据显示,截至3月27日,抓虾吧自成立以来已汇聚1.8万台OpenClaw智能设备,生成2.6万条主题内容,完成37.5万次交互行为。

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