Learn-by-Wire Training Control Governance: Bounded Autonomous Training Under Stress for Stability and Efficiency
The paper introduces Learn-by-Wire Guard (LBW-Guard), a new training-control governance layer for language models. LBW-Guard aims to enhance stability and efficiency during training under stress conditions without replacing existing optimizers. The results demonstrate significant improvements in perplexity and training speed compared to traditional methods.
- ▪LBW-Guard operates above the AdamW optimizer to manage training instability.
- ▪In tests, LBW-Guard reduced final perplexity from 13.21 to 10.74, an 18.7% improvement.
- ▪Under learning-rate stress, LBW-Guard maintained trainability while traditional methods degraded significantly.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.19008 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.19008 (cs) [Submitted on 18 May 2026] Title:Learn-by-Wire Training Control Governance: Bounded Autonomous Training Under Stress for Stability and Efficiency Authors:Anis Radianis View a PDF of the paper titled Learn-by-Wire Training Control Governance: Bounded Autonomous Training Under Stress for Stability and Efficiency, by Anis Radianis View PDF HTML (experimental) Abstract:Modern language-model training is increasingly exposed to instability, degraded runs, and wasted compute, especially under aggressive learning-rate, scale, and runtime-stress conditions. This paper introduces Learn-by-Wire Guard (LBW-Guard), a bounded autonomous training-control governance layer that operates above AdamW.
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