On the Direction of RLVR Updates for LLM Reasoning: Identification and Exploitation
Reinforcement learning with verifiable rewards (RLVR) has substantially improved the reasoning capabilities of large language models. While existing analyses identify that RLVR-induced changes are sparse, they primarily focus on the \textbf{magnitude} of these updates, largely overlooking their \textbf{direction}. In this work, we argue that the direction of updates is a more critical lens for understanding RLVR's effects, which can be captured by the signed, token-level log probability difference $Δ\log p$ between the base and final RLVR models. Through statistical analysis and token-replacement interventions, we demonstrate that $Δ\log p$ more effectively identifies sparse, yet reasonin...
| Authors | Kexin Huang, Haoming Meng, Junkang Wu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 23 March 2026 |
| Last updated at source | 23 March 2026 |
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