First, a difficulty curriculum across rollout phases. Our synthetic datasets label each datum by difficulty according to the number of hops required. Our training is divided into two phases across these difficulty levels as demonstrated in Beyond Ten Turns. During the first phase, the query distribution is skewed toward lower-difficulty questions. In the second phase, the distribution shifts toward higher-difficulty multi-hop tasks that require extended search trajectories and pruning cycles. This phasing allows a reasonable policy to be learned before exposing the model to problems where near-zero reward is likely without an already-competent search policy.
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