Reinforcement Learning Improves Traversal of Parametric Knowledge in LLMs

arXiv:2511.05933v2 Announce Type: replace Abstract: Reinforcement learning (RL) is often credited with improving language model reasoning at the expense of knowledge. We challenge this narrative by showing that reasoning models consistently outperform their instruction-tuned versions on pure knowledge recall tasks. These gains do not reflect newly acquired information, but rather an improved procedural skill in navigating and searching existing knowledge hierarchies within the model parameters. Structured prompting, which explicitly guides models through hierarchical traversal -- recovers most

Source: arXiv cs.CL — read the full report at the original publisher.

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