GRPO Training Dynamics for Small Language Models

Rajat Ghosh; Vaishnavi Bhargava; Henry Wong; Aryan Singhal; Debojyoti Dutta

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arXiv · 2609.39321

GRPO Training Dynamics for Small Language Models

Rajat Ghosh·Vaishnavi Bhargava·Henry Wong·Aryan Singhal·Debojyoti Dutta

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Abstract

Group Relative Policy Optimization (GRPO) has emerged as a memory-efficient reinforcement fine-tuning (RFT) technique for reasoning-intensive tasks. How- ever, GRPO training dynamics on small language models (SLMs) remain poorly understood, limiting its reliable adoption and reproducibility in open and resource- constrained environments. In this work, we present a systematic study of GRPO fine-tuning for SLMs ranging from 1.5B to 7B parameters under a practical single- node 8xA100 compute budget. Our study spans multiple model families and reasoning domains, including mathematics, coding, and multiple-choice question answering (MCQ) in science. Across these settings, we analyze how group size affects policy convergence, training stability, and downstream benchmark per- formance. We further characterize tensor-level update dynamics during GRPO training and investigate whether the choice of LoRA target modules and layers can improve the performance of GRPO-tuned models. While our initial GRPO-tuned models outperform their base counterparts on approximately 80% of mathematical benchmark evaluations, they demonstrate limited capability on MCQ and code reasoning tasks. Guided by our mechanistic evaluations, we refined our LoRA and reward-shaping configurations to improve performance in latter domains. These findings provide practical guidance for GRPO training for SLMs.

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BibTeX RIS

Rajat Ghosh, Vaishnavi Bhargava, Henry Wong, Aryan Singhal, Debojyoti Dutta. 2026-09-30. GRPO Training Dynamics for Small Language Models. https://arxiv.org/abs/2609.39321

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