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The cross-signal NTK is introduced, a token-level statistic that measures the alignment between reward and distillation gradients at position n and an empirical threshold beyond which naive mixing can lead to persistent training collapse is revealed.
GenMem is presented, which reformulates memory management as generative symbolic addressing, a multi-level discrete token tuple drawn from a Cartesian-product address space that factorizes a million-scale sparse memory space using fewer than one hundred discrete symbols.
This work proposes SERL-SQL, a selective execution-grounded reinforcement learning framework for multi-turn Text-to-SQL agents, showing that SERL-SQL produces high-quality candidates that can be reliably identified by lightweight execution-grounded rewards.
This work proposes AgenticRag-R1, a RL framework that deeply integrates reasoning, retrieval, and memory via a memory stack and fine-grained action space, supported by hierarchical action-aware rewards and an information-aware trajectory rejection strategy to enable effective long-horizon learning.
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