The paper delivers the first systematic taxonomy and hierarchical framework for data-efficient reinforcement learning post-training of large language models across data-centric, training-centric, and framework-centric views.
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A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions
The paper delivers the first systematic taxonomy and hierarchical framework for data-efficient reinforcement learning post-training of large language models across data-centric, training-centric, and framework-centric views.