Learn-by-Wire Training Control Governance: Bounded Autonomous Training Under Stress for Stability and Efficiency

Researchers introduced LBW-Guard, a bounded control layer above AdamW to stabilize language-model training without changing the objective.

In tests on Qwen2.5 models and WikiText-103, it improved perplexity and runtime versus standard AdamW.

Under harsher learning-rate stress, LBW-Guard remained stable while AdamW degraded, showing stronger training robustness.

The results suggest optimizer-level control can reduce failed or wasteful runs and improve compute efficiency for large-model training.