ISO: An RLVR-Native Optimization Stack
Merged summary
TL;DR - ISO is an RLVR-specific optimization framework that freezes a base language model’s weight spectra while updating its singular frames. It improves data-free specialist merging and reportedly reaches matched reasoning and coding accuracy with fewer training steps.
- Identifies “spectral inheritance”: RLVR can preserve base-model singular values while learning behavior through changes to input/output singular frames.
- ISO-Merger combines shared-base specialists without data, rollouts, gradient updates, or on-policy distillation.
- ISO-Optimizer supports base optimizers such as AdamW and Muon while keeping spectra fixed.
- On Qwen3-8B-Base, ISO-AdamW matched AdamW’s 0.495 aggregate accuracy in 100 versus 270 steps, then reached 0.509 after 210 steps.
Sources (1)
ISO: An RLVR-Native Optimization Stack
TL;DR - ISO is an RLVR-specific optimization framework that freezes a base language model’s weight spectra while updating its singular frames. It improves data-free specialist merging and reportedly reaches matched reasoning and coding accuracy with fewer training steps.
- Identifies “spectral inheritance”: RLVR can preserve base-model singular values while learning behavior through changes to input/output singular frames.
- ISO-Merger combines shared-base specialists without data, rollouts, gradient updates, or on-policy distillation.
- ISO-Optimizer supports base optimizers such as AdamW and Muon while keeping spectra fixed.
- On Qwen3-8B-Base, ISO-AdamW matched AdamW’s 0.495 aggregate accuracy in 100 versus 270 steps, then reached 0.509 after 210 steps.