Agentic RL Daily

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Every daily judgment should remain inspectable.

Dates are the primary index. Each edition is saved first as a stable snapshot, then the newest snapshot becomes the home page.

Type and topic filters help trace how the same research question evolves across papers, releases, systems work, and safety evidence.

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All TopicsAgent CapabilitiesAlignmentAsynchronous TrainingBlack-box AgentsContinual EvolutionData LoopsDistillationEngineering IterationKnowledge BoundariesMulti-turn AgentsMulti-turn InteractionMultimodalObservabilityOnline LearningOpen-source FrameworksProactive AgentsReward and Credit AssignmentReward DesignSearch AgentsSupervision SignalsSystems EngineeringSystems InfrastructureTest-time OptimizationTool UseTraining AlgorithmsTraining InfrastructureTraining StabilityUser ExperienceValue Functions安全与对齐记忆与自进化评估体系

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Dated editions

6 editions / 45 signals

VOL. 004

Agentic RL Daily

A dated Agentic RL Daily snapshot with 8 verified primary-source signals across papers, official releases, deployment evidence, and safety or alignment findings.

Training AlgorithmsAgent Capabilities安全与对齐评估体系Data Loops记忆与自进化
8 signals ->
VOL. 004

Daily Signals: The Internet taught us that the value of a network depends on \emph{how} its nodes connect: broadcast stars scale as $V\!\propto\!N$ (Sarnoff), fully-connected meshes as $N^2$ (Metcalfe), and group-forming networks as $2^{N}$ (Reed). We ask the analogous question for networks of AI agents. We model

A dated Agentic RL Daily snapshot with 6 verified primary-source signals across papers, official releases, deployment evidence, and safety or alignment findings.

Agent CapabilitiesReward and Credit AssignmentTraining AlgorithmsData LoopsSystems Engineering
6 signals ->