Resonant Convergence Analysis (RCA): Intelligent Early Stopping That Cuts Training Time by 35–45%

#opensource #machinelearning #deeplearning

Training deep-learning models often continues long after true
convergence, wasting GPU hours.\
Resonant Convergence Analysis (RCA) is a new open-source callback
that detects real convergence by analyzing oscillation patterns in
validation loss instead of relying on naive patience counters.

What is RCA?

RCA introduces two parameters:

Symbol Meaning Typical Range

β Resonance amplitude (training stability) 0--1
ω Resonance frequency (oscillation phase) ≈6 ± 0.5

Training stops when β ≥ 0.75 and oscillations flatten below a small
Δloss threshold.

Quick Example

from resonant_learner import ResonantCallback

rca = ResonantCallback(
    checkpoint_dir="./checkpoints",
    patience_steps=4,
    min_delta=0.003,
    ema_alpha=0.4,
    lr_reduction_factor=0.7,
    min_lr=1e-5,
    verbose=True,
)

for epoch in range(epochs):
    train_loss = train_epoch(...)
    val_loss = validate(...)
    rca(val_loss=val_loss, model=model, optimizer=optimizer, epoch=epoch)
    if rca.should_stop():
        print("RCA triggered early stopping.")
        break

Results (Production Validation)

Dataset Baseline RCA Compute Saved ΔAccuracy

MNIST 30 18 40% +0.12%
Fashion-MNIST 30 16 47% −0.67%
CIFAR-10 (ResNet-18) 60 45 25% +1.35%
BERT SST-2 10 7 30% −0.11%

Average compute reduction: ≈36%, accuracy preserved.

Installation

git clone https://github.com/Freeky7819/resonant-learner
cd resonant-learner
pip install torch torchvision

pip install -U pip setuptools wheel
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
pip install tqdm numpy pandas matplotlib timm transformers datasets

pip install -e .

pytest -q
python verify_installation.py

Reproduction Commands

CIFAR-10

python examples/cifar10_rca.py --epochs 60 --batch-size 128 --seed 42

BERT SST-2

python examples/hf_bert_glue.py --task sst2 --epochs 10 --batch-size 32 --seed 42

Learn More

📄 Scientific Validation Report on
Zenodo\
🔗 GitHub Repository\
🧠 Author: Damjan Žakelj --- Harmonic Logos

"Stop training when your model converges, not epochs later."

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