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$S^3$-R1: Learning to Retrieve and Answer Step-by-Step with Synthetic Data
Reinforcement learning (RL) post-training has enabled newer capabilities in models, such as agentic tool-use for search. However, these models struggle primarily due to limitations with sparse outcome-based rewards and a lack of training data that encapsulates questions of differing hardness, which
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