Research Paper#Reinforcement Learning, Large Language Models, KL Divergence, Regularization🔬 ResearchAnalyzed: Jan 3, 2026 23:59
KL Regularization in RL Training of LLMs: A Deep Dive
Published:Dec 26, 2025 04:20
•1 min read
•ArXiv
Analysis
This paper investigates the impact of different Kullback-Leibler (KL) divergence estimators used for regularization in Reinforcement Learning (RL) training of Large Language Models (LLMs). It highlights the importance of choosing unbiased gradient estimators to avoid training instabilities and improve performance on both in-domain and out-of-domain tasks. The study's focus on practical implementation details and empirical validation with multiple LLMs makes it valuable for practitioners.
Key Takeaways
- •Different KL divergence estimators used in RL training of LLMs can significantly impact performance.
- •Configurations with biased gradients can lead to training instabilities.
- •Unbiased gradient estimators generally lead to better performance.
- •KL regularization can stabilize off-policy RL training.
Reference
“Using estimator configurations resulting in unbiased gradients leads to better performance on in-domain as well as out-of-domain tasks.”