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Entropic Advantage Policy Optimization (EAPO) is introduced, an entropy-guided credit assignment method that treats success and failure asymmetrically and promotes more effective exploration, broadening problem coverage and generating more diverse candidate answers.
FlyBy is introduced, a selective querying framework, and 4B and 8B variants are trained to reason first, diagnose what remains unresolved, and, at a knowledge bottleneck, query stronger models whose parametric knowledge extends beyond its own.
Agent Memory Distillation (AMD), a training-free framework that transfers structured knowledge from a large teacher agent to a small student agent through hierarchical memory, is proposed, which consistently outperforms existing memory-based baselines.
Across 20 tasks from three public benchmarks, TS-Debate improves classification and question answering performance over strong baselines, while revealing that debate is most useful for global-structure and cross-view reasoning rather than local value reconstruction.
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