Sparse but Critical: A Token-Level Analysis of Distributional Shifts in RLVR Fine-Tuning of LLMs

1. 1Analyze token-level distributional shifts between base and RL models
2. 2Investigate the impact of token-level distributional shifts on model performance
3. 3Examine the sparse but critical nature of these shifts to inform fine-tuning strategies

ML researchers and engineers working on LLMs and RLVR fine-tuning can benefit from this study to improve model performance and reasoning capabilities

💡 Token-level distributional shifts in RLVR fine-tuning are sparse but critical to improving LLM performance

💡 Token-level analysis reveals sparse but critical changes in RLVR fine-tuning of LLMs