engineerkong/RL4LLM-Finetuning-SLR: RL4LLM Fine-Tuning SLR: Structured Extraction Dataset (Publications 2022–September 2025)

This release contains the structured data extraction sheets compiled during the systematic literature review of 230 peer-reviewed publications on Reinforcement Learning for Large Language Model (RL4LLM) fine-tuning, covering the period from 2022 to September 2025. The extraction was conducted following PRISMA guidelines and a hybrid human–LLM methodology.

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	0
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average

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