A machine learning practitioner experimented with applying the GRPO (Proximal Policy Optimization) algorithm to three large language models of varying sizes, trained from scratch. While pre-training showed expected improvements with scale, the GRPO fine-tuning phase yielded inconsistent and sometimes detrimental results. The smallest model (353M parameters) showed minimal impact, while a slightly smaller model (316M parameters) experienced a significant performance drop in perplexity, and the largest model (672M parameters) saw a minor degradation. AI
IMPACT Inconsistent results from GRPO fine-tuning suggest further research is needed to understand its application across different model scales and training setups.
RANK_REASON The item details an experiment with a specific LLM training technique (GRPO) and its outcomes on different model sizes, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]
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