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
The increased adoption and theoretical exploration of Reinforcement Learning with Verifiable Rewards (RLVR) in LLMs necessitates a deeper understanding of its training dynamics.
Understanding the 'linear regime' in RLVR training could lead to more efficient, stable, and predictable development of reasoning-oriented LLMs, accelerating their capabilities.
The observation of a consistent linear training regime in RLVR demystifies a previously 'black box' process, enabling better diagnostic tools and optimization strategies for LLM development.
- · AI Researchers
- · LLM Developers
- · AI Infrastructure Providers
Research into LLM training dynamics will accelerate, focusing on exploiting these linear properties.
Improved understanding could lead to more robust and explainable LLMs, increasing trust and adoption in critical applications.
The reduced 'black box' nature may democratize advanced LLM training techniques, broadening the field of innovation.
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Read at arXiv cs.LG