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

Anis Radianis

arXiv:2605.19008v1cs.AIcs.CLcs.LG

TL;DR

LLM training can become unstable, degrading runs and wasting compute, while optimizer-centric methods do not provide a runtime governance layer. The paper introduces LBW-Guard above AdamW and evaluates it across controlled stress tests, finding improved stability and productivity within a scoped training setting.

  • Problem

    Instability in language-model training can produce degraded runs and wasted compute, while optimizer-centric methods lack a governance layer for unstable runtime conditions.

  • Method

    LBW-Guard observes training telemetry, interprets operating regimes, and applies bounded control to AdamW execution without replacing the optimizer update rule.

  • Results

    LBW-Guard improves final perplexity across tested Qwen2.5 scales and preserves trainability under learning-rate stress where AdamW becomes severely degraded.

  • Takeaways & Limitations

    The results support a scoped systems conclusion that stability-sensitive LLM training can benefit from a governance plane above the optimizer.

  • Takeaways & Limitations

    The evidence is primarily from controlled single-GPU tests, with most experiments using LoRA and limited full-parameter validation.

Abstract

from arXiv · show

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. 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. 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. These results support a scoped systems conclusion that 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.

1 Introduction

LLM training is a fragile runtime process whose instability can waste compute and delay experimentation, but optimizer-centric methods lack a governance layer for responding to unstable conditions. LBW-Guard addresses this gap as a bounded control layer above AdamW.

  • Motivation: Training instability affects workloads across model scales, with larger and longer runs carrying greater accelerator, recovery, and experimentation costs.Aggressive learning rates and unfavorable training regimes can produce brittle trajectories even outside frontier-scale training.
  • Motivation: Operational reports document loss spikes, divergence, rollback, skipped batches, learning-rate reduction, and restart-based mitigation in large-model training.These examples connect optimization instability with recovery engineering and infrastructure complexity.
  • Problem: Optimizer-centric abstractions compute parameter updates but do not by themselves detect, interpret, or respond to unstable runtime conditions.The paper frames productive training under emerging instability as a systems problem in addition to an optimization problem.
  • Contribution: LBW-Guard adds sensing, regime interpretation, bounded control, actuation, and telemetry above AdamW while leaving AdamW as the optimizer.The governance layer mediates optimizer execution in response to runtime conditions rather than replacing the underlying update rule.
  • Contribution: The evaluation combines Qwen2.5 stress tests, model-size comparisons, gradient-clipping comparisons, and a no-LoRA TinyLlama sanity check.These experiments test robustness, abstraction-level distinctness, and whether the behavior depends structurally on adapter-based training.

2 Related Work

Prior work explains optimizer updates, local stabilization, and the operational costs of instability, but leaves room for a runtime governance layer that senses and governs training conditions. LBW-Guard occupies that systems-level space around AdamW.

  • Optimizer research: Optimizer research improves parameter-update computation, adaptive scaling, memory efficiency, and regularization behavior.The related work positions Adam, AdamW, Adafactor, and related methods as the dominant learning abstraction.
  • Stabilization research: Stabilization research addresses exploding or vanishing gradients, signal propagation, initialization sensitivity, convergence failures, and adaptive-optimization pathologies.These approaches primarily target local training pathologies or optimization behavior.
  • Operational instability: LLM training reports describe loss spikes, divergence, aggressive-learning-rate sensitivity, rollback, skipped batches, learning-rate reduction, and restarts as operational events.The examples motivate treating instability as a runtime condition rather than only a theoretical optimization concern.
  • Governance perspective: LBW-Guard asks whether training can be governed during execution by sensing instability early, interpreting operating regimes, and handling them through bounded control.This complements optimizer comparisons and pathology-specific stabilization methods.
  • Architecture: The architecture separates AdamW as the optimizer actuator from governance logic that monitors and modulates training execution.This separation preserves compatibility with existing optimizer infrastructure while adding observability and control.
  • Governance perspective: Unlike gradient clipping, LBW-Guard combines regime interpretation, bounded control postures, and run-level telemetry around optimizer execution.The clipping comparison tests whether ordinary local gradient suppression can explain the observed trainability preservation.

3 LBW-Guard: Component-Control Method

LBW-Guard wraps AdamW with a bounded governance loop that senses training telemetry, interprets operating conditions, selects constrained control, actuates the optimizer path, and logs behavior. The design emphasizes observable, bounded intervention rather than a new optimizer rule.

  • Architecture: LBW-Guard leaves AdamW responsible for parameter updates while the governance plane monitors trajectory, interprets conditions, and applies constrained control.The method is explicitly presented as a layer above AdamW, not as a redefinition of its update rule.
  • Sensing: The sensing layer is read-only, can use lightweight loss-only telemetry or sparse probing, and does not require full-gradient instrumentation.The sensor is not structurally tied to LoRA.
  • Control loop: The component loop proceeds through sensing, interpretation, policy selection, bounded actuation, and logging.The analyzer identifies stable, stressed, spike/oscillation, or recovery-like regimes before control is applied.
  • Interpretability: The specification connects empirical outcomes to observable control behavior while withholding implementation-specific policy logic.This preserves a public methodological boundary between reproducible architecture and proprietary controller details.
  • Control loop: At each step, bounded scale, damping, or release is applied to the AdamW execution path under predefined limits.The logger records control-active steps, regime switches, stress mode, scale, and control energy.

4 Experimental Design

The study uses controlled single-GPU Qwen2.5 training on WikiText-103 to test bounded governance across model sizes, learning-rate stress, clipping baselines, LoRA dependence, and telemetry. The design evaluates both language-model quality and compute productivity.

  • Setup: The evaluation uses Qwen2.5 variants, WikiText-103 raw, CUDA, PyTorch AdamW, and LoRA-based training stress tests in controlled single-GPU settings.The design tests bounded governance under controlled LLM training stress rather than frontier-scale pretraining.
  • Model comparisons: Qwen2.5-7B is the empirical anchor, with Qwen2.5-3B and Qwen2.5-14B comparisons testing whether effects persist across model-size presets.These comparisons are robustness checks, not a scaling-law study.
  • Metrics: Final validation perplexity is the main quality metric, supplemented by loss, wall-clock time, tokens per second, speedup, and governance telemetry.Runtime metrics reflect the study’s focus on productive compute under stress.
  • Stress tests: Learning-rate stress tests include 3 × 10^-3, 1 × 10^-3, and 5 × 10^-4 to examine trainability near fragile or degraded regimes.The tested rates are stress conditions, not recommended default recipes.
  • Baselines: Gradient-clipping baselines at LR = 10^-3 test whether ordinary local gradient suppression reproduces LBW-Guard’s trainability and perplexity improvements.LBW-Guard instead senses trajectories, assigns regimes, selects bounded postures, and records active control behavior.
  • Sanity check: A no-LoRA TinyLlama-1B full-parameter sanity check examines whether bounded governance remains useful without adapter-based training.The experiment is intentionally a sanity check rather than a full pretraining benchmark.

5 Results

Across matched stress-and-robustness tests, LBW-Guard improves final perplexity and, in several settings, runtime while preserving AdamW as the underlying optimizer. The strongest gains occur under learning-rate stress, where AdamW becomes severely degraded but LBW-Guard remains trainable; clipping alone does not reproduce this behavior.

  • Qwen2.5-7B Reference Setting: 18.7% lower final evaluation perplexity in Qwen2.5-7B, from 13.2086 under AdamW to 10.7353 under LBW-Guard.The result indicates improved final trajectory quality under the matched configuration, not merely avoidance of collapse.
  • Qwen2.5-7B Reference Setting: 991 control-active steps and 29 regime switches show that LBW-Guard actively monitors training and changes its bounded control posture during the run.The telemetry distinguishes the method from a passive wrapper around AdamW.
  • Model-Size Robustness: 6.3%, 18.7%, and 18.0% lower final perplexity occur in the 3B, 7B, and 14B presets, respectively.Runtime benefits vary by scale: the 3B setting has a 0.967× speedup, while the 7B and 14B settings improve both perplexity and runtime.
  • Learning-Rate Stress: At LR = 3 × 10−3, AdamW reaches 1885.24 final perplexity while LBW-Guard remains trainable at 11.5704; at LR = 10−3, the values are 659.76 and 10.3280.These are failure-sensitive stress cases rather than recommended training recipes; LBW-Guard keeps final perplexity within a usable range.
  • Gradient-Clipping Baseline: At LR = 10−3, clipped AdamW reaches 659.76 final perplexity at g = 1.0 and 891.37 at g = 0.5, whereas LBW-Guard reaches 10.3936.The comparison supports a different abstraction level: clipping suppresses gradient magnitude locally, while LBW-Guard governs runtime training regimes.

6 Discussion and Limitations

LBW-Guard’s stress-test results support training-control governance as a distinct layer above optimization, with evidence across model sizes, learning-rate stress, clipping comparisons, telemetry, and runtime efficiency. The discussion limits these conclusions to controlled, preliminary, and partially reproducible settings rather than universal training regimes.

  • Results: LBW-Guard preserves trainability under aggressive learning rates, where AdamW can consume compute while producing unusable or severely degraded perplexity.The authors frame this as preserving productive runs when trajectories become fragile, not merely improving final perplexity.
  • Results: LBW-Guard improves final perplexity across Qwen2.5 3B, 7B, and 14B settings, although the results do not establish a scaling law or frontier-scale equivalence.The larger relative gains occur in the 7B and 14B cases.
  • Results: The observed runtime gains are interpreted as improved training trajectory efficiency rather than more aggressive update scaling.With cmax = 1.0, the public configuration cannot speed training by amplifying optimizer steps beyond the base execution path.
  • Mechanism: Gradient clipping does not reproduce LBW-Guard’s reported trainability preservation because clipping limits gradient magnitude locally rather than governing optimizer execution from training-state information.The comparison is narrower than a claim that gradient clipping is generally ineffective.
  • Observability: Governance telemetry supplements loss and perplexity by recording control activity, regime changes, boundedness, and control effort, improving interpretation of how training responds.The paper presents telemetry as useful for distinguishing a governance effect from an unexplained performance improvement and for inspecting stability or recovery.
  • Limitations: The evidence is constrained by controlled single-GPU tests, predominantly LoRA training, undisclosed full controller policy, incomplete clipping sweeps, stress-oriented settings, and preliminary statistical validation.The authors call for broader full-parameter, distributed, cross-model, cross-dataset, longer-horizon, tuned-baseline, multi-seed, and independently reproduced studies.

7 Conclusion

LBW-Guard separates optimizer updates from runtime governance, using bounded control to improve stability-sensitive training while preserving AdamW. Across controlled stress tests, it improves perplexity across model scales, preserves trainability under learning-rate stress, and supports a scoped systems interpretation.

  • Architectural contribution: LBW-Guard governs the runtime conditions for AdamW updates rather than replacing the optimizer, reframing training stability as a systems problem.The governance layer senses and interprets training conditions while AdamW remains responsible for parameter updates.
  • Empirical findings: LBW-Guard improves final perplexity across Qwen2.5-7B, 3B, and 14B stress-test settings.The reported comparison spans the reference model and two additional model-size presets.
  • Empirical findings: Under learning-rate stress, LBW-Guard preserves trainability in regimes where AdamW becomes severely degraded.This result is part of the controlled stress-and-robustness evidence rather than a claim of universal optimizer superiority.
  • Empirical findings: Gradient-clipping comparisons suggest that LBW-Guard’s effect is not reducible to ordinary local gradient suppression.The comparison distinguishes bounded runtime governance from a conventional clipping baseline.
  • Empirical findings: The no-LoRA TinyLlama-1B full-parameter sanity check suggests that LBW-Guard’s behavior is not purely adapter-specific.The experiment extends the evaluation beyond the predominantly LoRA-based settings.
  • Scope and limitations: The evidence is limited to controlled single-GPU stress tests, mostly LoRA-based training, and one no-LoRA sanity check.The paper does not establish superiority across all optimizers, architectures, datasets, hardware environments, or frontier-scale distributed pretraining regimes.
  • Scope and limitations: Future evaluation should include broader model families, full-parameter and distributed training, longer horizons, additional datasets, stronger baselines, and seed-based validation.These extensions would test whether training-control governance generalizes as an architectural layer.
  • Conclusion: The paper presents an initial empirical case for Learn-by-Wire training in which learning is optimized, sensed, interpreted, and governed.Its broader implication is a layered training architecture that complements optimizer design with a governance plane.

Broader Impact

LBW-Guard’s broader impacts are primarily operational and environmental, but improved training efficiency could also lower the cost of developing capable models. The paper therefore frames benefits and risks as dependent on how the governance method is used.

  • Positive impacts: LBW-Guard may reduce wasted accelerator time, lower experimentation cost, and improve model-development workflow reliability.These potential benefits follow from reducing failed or severely degraded runs.
  • Potential risks: More efficient training infrastructure may reduce the cost of developing capable models, including models that could be misused.The paper characterizes these potential negative impacts as indirect.
  • Scope: The empirical claims are scoped to controlled stress-test settings rather than deployment of a public model.The evaluated contribution is a training-control method, not a released pretrained model, dataset, or application-facing system.
  • Governance boundary: Responsible use may improve resource efficiency, reliability, and transparency, while organizationally unguided efficiency gains could accelerate risky model development.Downstream risks require separate evaluation and oversight.

Assets, Licenses, and Compute Resources

The experiments use public model, dataset, and software assets, while the evaluated LBW-Guard implementation remains proprietary. A public Colab script supports workflow inspection but only partial reproducibility.

  • Assets and licenses: The experiments use Qwen2.5 variants, TinyLlama, WikiText-103, CUDA, PyTorch AdamW, and LoRA under their corresponding licenses.Users should comply with the applicable model, dataset, and software license terms.
  • Implementation availability: The LBW-Guard implementation is proprietary and is not released with the submission.The paper reports the component-level method specification, public controller configuration, and experimental results.
  • Reproducibility: A public Colab test script archived on Zenodo supports inspection of the experimental workflow and execution pathway.The artifact complements the method specification and reported result tables.
  • Reproducibility: Code-level reproducibility is partial because the full proprietary LBW-Guard controller implementation is not disclosed.The public artifact does not expose the complete controller.

A Base Run Reference Settings

Table 8 provides the main public experimental reference settings used across the controlled stress-and-robustness experiments. It clarifies the experimental boundary while withholding the proprietary controller policy.

  • Reference configuration: Table 8 reports the main experimental reference settings used across the controlled stress-and-robustness experiments.The settings are provided to support reproducibility.
  • Reference configuration: The table clarifies the experimental boundary, while the proprietary LBW-Guard controller policy is not fully disclosed.The public configuration should therefore be distinguished from the complete controller implementation.

B Public Execution Interface

The public interface keeps AdamW-facing configuration unchanged between baseline and governed variants while exposing additional bounded control settings. It documents reproducible component-level inputs without revealing the full controller policy.

  • LBW-Guard receives the same trainable parameters, learning rate, AdamW momentum coefficients, and weight decay as the AdamW baseline.
  • The interface example is intended for component-level reproducibility rather than disclosure of the full proprietary controller policy.
  • The governed interface adds settings for sensing frequency, stress and spike detection, recovery behavior, exponential moving average smoothing, and telemetry.

C Reproducibility Artifact

The paper archives a lightweight Colab artifact to support inspection of its workflow and comparison procedure. Because the artifact is partial and environment-dependent, it supports methodological transparency rather than exact replication.

  • A lightweight Colab-based reproduction artifact is archived on Zenodo for external inspection of the workflow, configuration, evaluation pathway, and optimizer comparison procedure.
  • The artifact is not the full internal experiment code used to produce every reported result.
  • Different hardware, runtime environments, library versions, random seeds, and resource constraints may produce different numerical values.
  • The proprietary controller policy is not fully disclosed, although the paper provides public specifications, settings, result tables, telemetry categories, and bounded-control interpretation.
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