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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.
LoongReflect is proposed, a training framework that formulates reflection as a memory-control policy over a reversible trajectory tree using explicit reflect and backtrack actions and demonstrates consistent improvements over outcome-only reinforcement learning and self-distillation baselines.
A novel framework that leverages the language understanding and code generation ability of Large Language Models (LLMs) to build an information retrieval system with Visualization-oriented Natural-language-based Inter-faces (V-NLI).
Harness-RL is introduced, a structured reinforcement learning framework that combines Conflict-Aware Policy Optimization (CAPO) with interface-level black-box trajectory construction and supports both central-only and joint multi-agent training.
ScaffoldAgent is proposed, a utility-guided dynamic outline optimization framework for OEDR that models outline evolution as a structured decision process with three operations: Expansion, Contraction, and Revision, enabling controlled updates to the report scaffold.
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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