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In the 7B reference setting, LBW-Guard reduces final perplexity from 13.21 to 10.74, an 18.7% i","title":"Learn-by-Wire Training Control Governance: Bounded Autonomous Training Under Stress for Stability and Efficiency","url":"https://arxiv.org/abs/2605.19008","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.19008v1 Announce Type: new \nAbstract: 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. Rather than replacing the optimizer update rule, LBW-Guard observes training telemetry, interprets instability-sensitive regimes, and applies bounded control to optimizer execution while preserving fixed training objectives.\n  We evaluate LBW-Guard in a Qwen2.5-centered stress-and-robustness suite using WikiText-103, with Qwen2.5-7B as the empirical anchor, model-size comparisons against Qwen2.5-3B and Qwen2.5-14B, learning-rate stress tests, gradient-clipping baselines, and a no-LoRA TinyLlama-1B full-parameter sanity check. 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