Experiments & Analysis
A comprehensive guide to validation protocols, practical libraries, and analytical metrics for evaluating optimization algorithms across various domains.
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Computer Vision
Image Classification
Classification remains the primary testbed for verifying the convergence speed and generalization capability of new optimizers.
Standard Benchmarks
- CIFAR-10/100: For initial debugging and small-scale verification.
- ImageNet-1k: The gold standard for large-scale regime verification.
Backbone Models
Recommended Repositories
Generative Models
Evaluating stability and sample quality in generative tasks, which often involve complex loss landscapes.
Tasks & Models
- Diffusion: DDPM, Stable Diffusion (Latent Diffusion).
- DiT: Diffusion Transformers (Sora, Stable Diffusion 3).
- GANs: StyleGAN (legacy comparisons).
Key Metrics
Recommended Repositories
Object Detection & Segmentation
Verifying optimization in multi-task settings (classification + localization) with complex loss functions.
Standard Benchmarks
- COCO: Common Objects in Context.
- LVIS: Large Vocabulary Instance Segmentation.
Metrics
Recommended Repositories
Large Language Models (LLM)
Pre-training & Fine-tuning
Validating optimizers on LLMs requires handling massive scale, distributed training stability, and long-horizon convergence.
Datasets
C4 (Colossal Clean Crawled Corpus), The Pile, RedPajama.
Architectures
GPT-2/3 (Decoder-only), LLaMA, OPT, BERT (Encoder-only).
Training Schemes
Full Pre-training, LoRA/QLoRA (Parameter-Efficient Fine-Tuning), Instruction Tuning.
Recommended Repositories
Evaluation Metrics
- Perplexity (PPL) Lower is better
- Downstream Accuracy MMLU, HellaSwag, GSM8K
- Training Stability Loss spikes, divergence checks
Advanced Analysis
- Scaling Laws Fit power laws \( L(N) \approx C N^{-\alpha} \) to predict performance at scale.
- Instruction Tuning & RLHF Verify optimizer effectiveness in alignment stages (SFT, DPO, PPO).
Multimodal LLMs (MLLM)
Validating optimization in multimodal settings involves ensuring alignment between different modalities (e.g., Vision and Language) without catastrophic forgetting of individual capabilities.
Common Models
LLaVA, CLIP, BLIP-2, Flamingo
Tasks
Visual Question Answering (VQA), Image Captioning, Text-to-Image Retrieval
Metrics
VQA Accuracy, CIDEr (Captioning), Recall@K (Retrieval)
Recommended Repositories
Reinforcement Learning (RL)
RL optimization presents unique challenges like non-stationary data distributions, high variance in gradient estimates, and the need for exploration-exploitation balance.
On-Policy Methods
Optimization must be stable as data is generated by the current policy.
Off-Policy / Offline
Learning directly from preference datasets without reward modeling.
Recommended Repositories
Practical Libraries for Experimentation
Hugging Face TRL
Full-stack library for Reinforcement Learning with Transformers (SFT, RM, PPO, DPO).
Unsloth
Optimized fine-tuning library that makes LLM training 2-5x faster with 70% less memory.
Weights & Biases
The industry standard for experiment tracking, dataset versioning, and model visualization.
LM Evaluation Harness
A unified framework to test generative language models on a large number of different evaluation tasks.
timm
A collection of image models, layers, utilities, optimizers, schedulers, and data-loaders.
Optuna
An automatic hyperparameter optimization software framework, particularly designed for machine learning.
Accelerate
A library that enables the same PyTorch code to be run across any distributed configuration with ease.
PEFT
State-of-the-art parameter-efficient fine-tuning methods (LoRA, Prefix Tuning, P-Tuning) for large models.
bitsandbytes
Lightweight wrapper around CUDA custom functions, enabling 8-bit optimizers and quantization.
Analysis Methods & Metrics
Convergence Rate
Measure how quickly the training loss decreases. Analyze iterations to reach specific loss thresholds.
Generalization Gap
The difference between Training and Test Accuracy. Smaller gap indicates better generalization (flatter minima).
Hyperparam Sensitivity
Robustness to learning rate, weight decay, and momentum. Visualize the "sweet spot" size.
Computational Efficiency
Wall-clock time per iteration and peak memory usage. Throughput translates directly to training cost.
Hessian Spectrum
Analyze top eigenvalues (\(\lambda_{max}\)). Lower \(\lambda_{max}\) typically correlates with better generalization.
Rep. Similarity (CKA)
Centered Kernel Alignment measures similarity between learned features layer-by-layer.