Linear Dynamics in the RLVR Training of Large Language Models

arXiv:2601.04537v3 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has driven significant performance gains in reasoning-oriented large language models (LLMs), yet its internal training dynamics remain largely a black box. In this work, we perform a comprehensive trajectory-level analysis of RLVR and uncover a striking regularity: across various model families, RL algorithms, and training configurations, RLVR consistently enters a robust linear regime, where both parameter weights and output log-probabilities, measured rigorously via teacher-forced evalua

Source: arXiv cs.LG — read the full report at the original publisher.

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