These trajectories are filtered before training based on two recall metrics: trajectory recall (the fraction of target chunks encountered at any point during search) and output recall (the fraction of target chunks present in the final document set). We include both successful and unsuccessful rollouts in the SFT dataset. This is motivated by Shape of Thought, which demonstrates that training on synthetic traces from more capable models improves performance even when all traces lead to incorrect final answers, as the distributional properties of the traces matter more than the correctness of every individual step. In our setting, low-recall trajectories still contain well-formed tool calls, query decompositions, and pruning decisions that provide useful behavioral signals.
（本文来源：世界模型工场，钛媒体获准刊载）
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