LBW-Guard: Bounded Autonomous Training Control Governance for Stable LLM Training Under Stress

Forum topic · 小凯 · 2026-05-21

Summary

LBW-Guard (Learn-by-Wire Guard) is a bounded autonomous training-control governance layer that operates above the AdamW optimizer to address instability, degraded runs, and wasted compute in language-model training under aggressive learning-rate, scale, and runtime-stress conditions. Rather than replacing the optimizer update rule, it observes training telemetry, interprets instability-sensitive regimes, and applies bounded control to optimizer execution while preserving fixed training objectives. Evaluated on a Qwen2.5-centered WikiText-103 stress-and-robustness suite (Qwen2.5-7B anchor, comparisons with Qwen2.5-3B/14B, learning-rate stress tests, gradient-clipping baselines, and a TinyLlama-1B full-parameter sanity check), LBW-Guard reduces 7B final perplexity from 13.21 to 10.74 (18.7% improvement) and cuts end-to-end time from 392.54s to 357.02s (1.10x speedup). Under LR=3e-3 stress, AdamW degrades to perplexity 1885.24 while LBW-Guard stays trainable at 11.57; gradient clipping does not reproduce this effect. Results support a governance plane above the optimizer for stability-sensitive LLM training.

Overview

Research areas: cs.AI, cs.CL, cs.LG Author: Anis Radianis Published: 2026-05-21 arXiv: 2505.01253

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.

Evaluation

LBW-Guard is evaluated in a Qwen2.5-centered stress-and-robustness suite using WikiText-103:

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
A no-LoRA TinyLlama-1B full-parameter sanity check

Key Results

In the 7B reference setting, LBW-Guard reduces final perplexity from 13.21 to 10.74 (an 18.7% improvement) while reducing end-to-end time from 392.54s to 357.02s (a 1.10x speedup).
Under stronger learning-rate stress, AdamW degrades to 1885.24 final perplexity at LR=3e-3 and 659.76 at LR=1e-3, whereas LBW-Guard remains trainable at 11.57 and 10.33, respectively.
Gradient-clipping baselines do not reproduce this effect.

Conclusion

These results support a scoped systems conclusion: stability-sensitive LLM training can benefit from a governance plane above the optimizer. LBW-Guard provides evidence that bounded runtime control can preserve productive compute under stress while remaining distinct from optimizer replacement and local gradient suppression.