ISO: An RLVR-Native Optimization Stack
Reinforcement learning with verifiable rewards (RLVR) allows models to learn new behaviors by changing input and output frames while reusing the base model's weight spectra. This study formalizes this 'spectral inheritance' into Isospectral Optimization (ISO), a native optimization stack. ISO-Merger handles offline expert merging, and ISO-Optimizer handles online training, showing that inheriting the spectrum while optimizing frames leads to highly efficient learning.