| title | Spectral Rewiring for Exploration, Purification, and Model Merging |
|---|---|
| authors | Zhilong Zhang, Hongli Yu, Huan-ang Gao, Hanlin Wu, Yuxuan Song, Wei-Ying Ma, Ya-Qin Zhang, Hao Zhou |
| date | 2026-07-03 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.03065 |
| upvotes | 8 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | Reinforcement learning has become a standard post-training recipe for large language models, but dense full-parameter updates create two deployment-relevant bottlenecks: suppressed reasoning performance, often reflected by premature saturation of test-time scaling, and interference when consolidating multiple capabilities through multi-domain training or model merging. We show that the reasoning-effective component of these updates is largely concentrated in the base model's spectral space, motivating Subspace-Aligned Rewiring (SAR), a post-hoc editing method that retains this spectral core while removing orthogonal components. SAR therefore preserves reasoning gains and filters residual update directions that suppress performance or amplify cross-domain interference. Across several model families and scales, SAR extracts compact reasoning cores using as little as approximately 0.58% of total parameters: it preserves over 99% of post-training performance and improves high-k exploration in mathematical reasoning, and generalizes to agentic coding by improving six of seven open benchmarks on an in-house model. SAR also purifies mixed-domain training updates by releasing suppressed coding capability while maintaining math reasoning and instruction following. It further enables model merging across experts, yielding cross-domain generalization that surpasses previous merging baselines and even the best single-domain experts. Overall, SAR shows that extracting reasoning-effective updates from parameter geometry can serve as a training-free mechanism to improve reasoning and multi-domain performance. |
cite
@misc{zhang2026spectral,
title={Spectral Rewiring for Exploration, Purification, and Model Merging},
author={Zhilong Zhang and Hongli Yu and Huan-ang Gao and Hanlin Wu and Yuxuan Song and Wei-Ying Ma and Ya-Qin Zhang and Hao Zhou},
year={2026},
eprint={2607.03065},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.03065}
}