LBW-Guard: Bounded Autonomous Training Control for Language Models
A recent study presents Learn-by-Wire Guard (LBW-Guard), an autonomous governance layer designed to enhance the training control of language models above AdamW, ensuring stability during high-stress scenarios. LBW-Guard monitors training telemetry, identifies regimes prone to instability, and implements bounded control over optimizer execution while maintaining fixed training goals. The evaluation utilized a Qwen2.5-focused stress-and-robustness framework with WikiText-103, using Qwen2.5-7B as the primary reference, alongside comparisons to Qwen2.5-3B and Qwen2.5-14B, learning-rate stress assessments, gradient-clipping benchmarks, and a no-LoRA TinyLlama-1B full-parameter validation. In the 7B reference scenario, LBW-Guard achieved a reduction in final perplexity from 13.21 to 10.74, marking an 18.7% enhancement. The study tackles issues of instability, inefficient runs, and excessive computation under high learning rates, scale, and runtime stress.
Key facts
- LBW-Guard is a bounded autonomous training-control governance layer above AdamW.
- It observes training telemetry and applies bounded control to optimizer execution.
- Evaluation uses Qwen2.5 models (3B, 7B, 14B) on WikiText-103.
- Includes learning-rate stress tests, gradient-clipping baselines, and TinyLlama-1B sanity check.
- In 7B setting, LBW-Guard reduces perplexity from 13.21 to 10.74 (18.7% improvement).
- Addresses instability under aggressive learning-rate, scale, and runtime-stress conditions.
- Preserves fixed training objectives while applying control.
- Published on arXiv with ID 2605.19008.
Entities
Institutions
- arXiv