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The method repurposes DR-RL trajectories, which naturally contain search histories, visited webpages, evidence snippets, and final-answer supervision, and replaces the compact snippets and webpage summaries in each trajectory with the full contents of their corresponding URLs, producing substantially longer multi-docum...
ADRS is introduced, a framework for constructing return-associated token-level credit for multi-turn language agents that centers and normalizes privileged token scores within each step, modulates them with a return-associated Teacher Value Advantage gate based on within-group confidence--return association, and incorp...
The proposed LLM agent early-stopping cascade outperforms the best single-gate baseline in every model-environment pair, saving 1.5-8.8 times more compute at a 90% recall target.
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