Balancing Diversity and Precision in LLM Next Token Prediction
Published:Dec 28, 2025 14:53
•1 min read
•ArXiv
Analysis
This paper investigates how to improve the exploration space for Reinforcement Learning (RL) in Large Language Models (LLMs) by reshaping the pre-trained token-output distribution. It challenges the common belief that higher entropy (diversity) is always beneficial for exploration, arguing instead that a precision-oriented prior can lead to better RL performance. The core contribution is a reward-shaping strategy that balances diversity and precision, using a positive reward scaling factor and a rank-aware mechanism.
Key Takeaways
- •Proposes a method to reshape the pre-trained token-output distribution for better RL exploration.
- •Introduces a reward-shaping strategy that balances diversity and precision.
- •Finds that a precision-oriented prior can be more beneficial for RL than a diversity-focused one.
Reference
“Contrary to the intuition that higher distribution entropy facilitates effective exploration, we find that imposing a precision-oriented prior yields a superior exploration space for RL.”