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What is training

What is training

Training is the process of teaching an AI model by exposing it to data so it learns patterns, relationships, and structures. Before training, a model is a blank structure with random parameters — it cannot classify images, generate sentences, or make predictions. After training, those parameters encode knowledge extracted from the data. Training is what transforms an empty neural network into a useful tool.

Think of it like teaching someone to cook. You start with a person who has never held a knife. You give them thousands of recipes (the dataset), a kitchen to practice in (the compute), and a chef who tastes each dish and says what went wrong (the loss function). After enough practice — burning meals, over-salting soups, adjusting technique — the cook can improvise a dish from ingredients they have never combined before. That improvisation is what a trained model does during inference.

In short: training is the "studying" phase — the model reads millions of examples and adjusts itself until it can handle new, unseen inputs. The trained model is the knowledge gained.

How training works step by step

Although end users only see a finished model, training is a multi-stage engineering pipeline. Each stage introduces decisions that affect the final result.

Data collection

Every training run begins with data. The project must assemble a dataset that is relevant to the task the model will perform. For a text model, this might be a web-scale corpus of natural language. For a crypto-specific model, it might include blockchain transaction histories, protocol documentation, or social sentiment data. The quality, size, and relevance of the dataset set the upper bound on what the model can learn — no architecture can compensate for missing or corrupt data.

Data preprocessing

Raw data is rarely usable as-is. Preprocessing transforms it into a uniform format the model can ingest. Common steps include tokenization (splitting text into sub-word units), normalization (scaling numerical values), deduplication (removing near-identical records), and filtering (removing low-quality, offensive, or irrelevant examples). Poorly preprocessed data introduces noise that the model may memorize rather than learn from.

Architecture selection

Before training begins, engineers choose a model architecture — the blueprint that defines how data flows through the model. For text tasks, the dominant architecture today is the transformer, which powers large language models. For image tasks, convolutional neural networks (CNNs) and vision transformers (ViTs) are common. The architecture determines the number and type of layers, how attention is computed, and how the model's capacity scales with size. Many crypto AI projects skip this step by adopting an existing open-source architecture (DeepSeek, Mistral, Stable Diffusion) and proceeding directly to fine-tuning.

Training loop

The training loop is the core optimization cycle that runs for hours, days, or weeks:

  1. The model receives a batch of preprocessed data.
  2. It produces an output — a prediction, a generated token, a classification.
  3. The output is compared to the expected result using a loss function, which calculates the error.
  4. An optimization algorithm (typically a variant of stochastic gradient descent) computes how each parameter should change to reduce the error.
  5. The model's internal parameters (weights) are adjusted slightly in the direction that lowers the loss.
  6. The cycle repeats for the next batch.

One full pass through the entire dataset is called an epoch. Large models may train for hundreds of epochs on massive datasets. The training loop is the most compute-intensive stage — a single large language model pre-training run can consume thousands of GPUs running continuously for months.

Evaluation

Throughout training, engineers periodically evaluate the model on a held-out validation set — data the model has never seen during training. This reveals whether the model is actually learning generalizable patterns or merely memorizing training examples (a problem called overfitting). Evaluation uses benchmarks and quality metrics appropriate to the task: perplexity for language models, accuracy or F1 for classifiers, BLEU or ROUGE for translation and summarization.

Deployment

Once a model reaches acceptable quality on evaluation metrics, it is packaged for production. This may involve converting the model to a more efficient format, applying quantization (reducing numerical precision to cut memory and compute requirements), and deploying it to an inference pipeline. The trained model — the collection of learned weights — is the artifact that actually serves users.

Stage What happens Example
Data collection Assemble relevant dataset Gather web text, code, blockchain data
Preprocessing Clean and format data Tokenize text, remove duplicates
Architecture selection Choose model blueprint Select transformer with 7B parameters
Training loop Optimize weights via gradient descent Run for 200 epochs on 1024 GPUs
Evaluation Test on unseen data Measure perplexity on validation set
Deployment Package model for production Quantize weights, deploy to API

Types of training

Not all training is the same. Different types serve different purposes and appear at different stages of the AI lifecycle.

Pre-training

Pre-training means training a model from scratch on a massive, general-purpose dataset. The goal is to learn broad patterns — language structure, visual features, statistical regularities — rather than task-specific knowledge. Pre-training is by far the most expensive stage. Training a frontier LLM from scratch can cost tens of millions of dollars and require thousands of high-end GPUs running for months. Very few crypto AI projects do this; the vast majority use pre-trained models from the open-source ecosystem and adapt them.

Fine-tuning

Fine-tuning takes a pre-trained model and adjusts it on a smaller, domain-specific dataset. This is the most common approach in crypto AI. A project might fine-tune an LLM on blockchain documentation, smart contract code, or DeFi protocol data to improve its performance on crypto-specific questions. Fine-tuning is far cheaper than pre-training because it starts from an already capable model and only needs to shift weights rather than learn from zero.

Reinforcement learning from human feedback (RLHF)

RLHF is a post-training technique used to align a model's outputs with human preferences. After pre-training and initial fine-tuning, human evaluators rank multiple model outputs for the same input. These rankings train a reward model, which then guides further training of the main model via reinforcement learning. RLHF is the technique that makes chatbots follow instructions, avoid harmful content, and produce responses humans find helpful. It adds a human judgment layer on top of purely statistical learning.

Knowledge distillation

Distillation trains a smaller "student" model to reproduce the outputs of a larger "teacher" model. The student learns not from the original data but from the teacher's predictions, which contain richer information (soft probability distributions rather than hard labels). Distillation is practical for projects that need to run models on limited hardware — such as edge devices or cost-constrained decentralized inference networks — while preserving most of the teacher model's quality.

Parameter-efficient fine-tuning (LoRA and adapters)

LoRA (Low-Rank Adaptation) and similar techniques freeze most of the pre-trained model's weights and train only a small set of additional parameters. This makes fine-tuning dramatically cheaper in compute and memory — often possible on a single consumer GPU. LoRA adapters are lightweight files (often just tens of megabytes) that can be loaded on top of a base model to customize its behavior for a specific task. This pattern is particularly relevant to decentralized AI, where participants may want to customize models without bearing the full cost of fine-tuning all weights.

Distributed and federated training

Distributed training splits the workload across multiple GPUs or nodes, either by dividing the data (data parallelism) or the model itself (model parallelism). Federated training goes further: data stays on each participant's device and only model updates (gradients) are shared. The model improves collectively without any single party accessing all the data. This pattern aligns with decentralization principles and appears in projects within the AI/Training category.

Type Cost Typical use in crypto AI
Pre-training Very high (millions of dollars) Rare; most projects use existing models
Fine-tuning Moderate (hundreds to thousands of dollars) Common; adapting models to crypto domain
RLHF High (requires human evaluators) Uncommon; seen in agent-oriented projects
Distillation Moderate Used to create lightweight models for inference
LoRA / adapters Low (single GPU) Growing; enables per-user customization
Distributed / federated Varies Core pattern for decentralized training networks

Why training matters for verifiability

A project that trains its own models demonstrates deeper AI capability than one that only calls a third-party API. Training implies the project has access to relevant data, compute resources, and machine learning expertise. It also produces verifiable artifacts: training logs, loss curves, model checkpoints, and benchmark results.

In DecAIHub project cards, training-related values in the AI component field — such as distributed training, fine-tuning, model training pipeline — indicate that the project does more than serve pre-built models. The training component page describes how these values appear in practice.

What training requires

Three resources define whether training is feasible:

  • Data — a quality dataset that is large enough and relevant to the task. Data quality determines model quality.
  • Compute — GPUs or specialized hardware. More data and larger models need more compute. This is the main cost driver.
  • Expertise — choosing architectures, tuning hyperparameters, evaluating results, and debugging failures requires machine learning knowledge.

Projects that claim to offer training infrastructure should show evidence of all three.

Where training appears in DecAIHub project cards

Training surfaces in multiple parts of a DecAIHub project card:

  • AI component — training-related values such as "distributed training," "fine-tuning," or "model training pipeline" indicate that the project actively trains or supports model training rather than only consuming pre-built models.
  • Category — projects in the AI/Training category focus specifically on providing training infrastructure or services.
  • Evidence table — Tier-1 evidence for training includes public training logs, loss curves, model checkpoints released to Hugging Face or similar repositories, and published benchmark comparisons.
  • ai_score — within the DecAI Fit algorithm, demonstrating real training with published evidence raises the ai_score. Merely claiming "we train models" without artifacts does not.

What training alone does not prove

  • Training a model does not mean the model is good. Without benchmarks or evaluation, there is no way to assess quality.
  • Training does not prove the model is in use. A model that was trained but never deployed for inference produces no value for users.
  • Training on someone else's open-source model is valid but should be disclosed, not presented as building from scratch.
  • Training does not explain why a token is necessary for the process.
  • Claiming "decentralized training" without evidence of multiple independent participants actually contributing compute is unverifiable. Check for on-chain records of training tasks, provider registrations, or verifiable compute logs.

Check the evidence table and Tier-1/2/3 evidence when evaluating training claims.

Do not confuse with

  • Inference is the next stage — running the trained model on new data. Training produces the model; inference uses it.
  • Training as an AI component describes how training appears in DecAIHub project cards. This page explains the concept itself.
  • AI/Training category classifies projects focused on training infrastructure. The concept of training applies more broadly.
  • Data collection and labeling prepares the data that training consumes. They are related but distinct stages.
  • AI model is the artifact produced by training — the set of learned weights. Training is the process; the model is the result.

Frequently asked questions

How long does AI training take?

It depends on the model size, dataset size, and available hardware. Fine-tuning a small model on a focused dataset can finish in hours on a single GPU. Pre-training a frontier LLM from scratch takes weeks to months on clusters of thousands of GPUs. For reference, training a frontier 70B-parameter LLM requires approximately 1–2 million GPU-hours on NVIDIA A100 hardware. Distributed and federated training can reduce wall-clock time by spreading the workload but introduce coordination overhead.

What is the difference between pre-training and fine-tuning?

Pre-training teaches a model general knowledge from a massive, broad dataset — it learns language structure, visual features, or statistical patterns from scratch. Fine-tuning takes that pre-trained model and specializes it on a smaller, domain-specific dataset so it performs better on a particular task. Pre-training is expensive and done once; fine-tuning is cheaper and can be repeated for different domains.

Why is training so expensive?

The dominant cost is compute hardware. Training large models requires thousands of high-end GPUs running continuously, consuming significant electricity. Data preparation, human labeling, and expert engineering time add further costs. Pre-training a single frontier model can cost tens of millions of dollars. Techniques like LoRA, distillation, and transfer learning via fine-tuning reduce costs dramatically by reusing existing pre-trained models rather than starting from zero.

Can you train AI on a blockchain?

Running a full training loop directly on-chain is impractical — blockchain execution environments are far too slow and expensive for the billions of matrix operations training requires. However, blockchains can coordinate training. Decentralized training networks use on-chain mechanisms to assign tasks, verify contributions, distribute rewards, and record provenance, while the actual computation happens off-chain on participating GPUs. This pattern appears in projects within the AI/Training category.

What is RLHF?

Reinforcement learning from human feedback is a post-training technique that aligns a model's behavior with human preferences. After initial training, human evaluators rank the model's outputs. These rankings train a reward model, which then guides further optimization of the main model through reinforcement learning. RLHF is what makes modern chatbots follow instructions and produce helpful, safe responses rather than raw statistical completions.

See also

External references

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