ISO: An RLVR-Native Optimization Stack
Reinforcement learning with verifiable rewards (RLVR) is rapidly advancing the reasoning capabilities of language models, yet the optimization layer that converts reward feedback into weight-space updates remains poorly understood. Building on our prior analysis (Zhu et al., 2025), we study this missing layer through the singular structure of model weights and identify spectral inheritance: RLVR can reuse the base model's weight spectra while acquiring new behavior through changes in the associated input and output singular frames. We operationalize spectral inheritance as Isospectral Optimi
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- PossiblePossibly related (embedding) · 54%RLHF →
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- LinkedLinked via arxiv author · 85%Hanqing Zhu →
“ISO: An RLVR-Native Optimization Stack”
- LinkedLinked via arxiv author · 85%Wenyan Cong →
“ISO: An RLVR-Native Optimization Stack”
- LinkedLinked via arxiv author · 85%Zhizhou Sha →
“ISO: An RLVR-Native Optimization Stack”
- LinkedLinked via arxiv author · 85%Sagnik Mukherjee →
“ISO: An RLVR-Native Optimization Stack”