What Are the Two Main Types of LLM Training?
You're building an AI system that needs to understand natural language — maybe for controlling IoT devices, maybe for parsing sensor logs, maybe for a chatbot that doesn't hallucinate your customer's order history. You've got a budget, a deadline, and a nagging question: what are the two main types of llm training?
I've been on the ground with this at SIVARO since 2021, when we started training custom models for industrial IoT clients. We burned through $400k in GPU credits before we figured out what actually works. Here's the honest answer.
There are two main types: pre-training and post-training (which includes fine-tuning, instruction tuning, and alignment). Most people think pre-training is the hard part. They're wrong. The hard part is knowing when to stop pre-training and start post-training.
This guide will walk you through both types, the trade-offs no one talks about, and the practical decisions you'll face if you're building production AI systems today — especially for domains like IoT where latency, reliability, and cost matter more than benchmark scores.
Pre-Training: Building the Raw Model
Pre-training is the phase where you take a massive pile of text — think 10 trillion tokens, the entire internet scraped and deduplicated — and train a transformer from scratch to predict the next word. This is what OpenAI, Google, Meta, and the open-source community have been doing for years.
The output is a base model. It knows language structure, facts, reasoning patterns, but it doesn't follow instructions. It's like a teenager who knows everything but has zero social skills. You can't just deploy it.
What Actually Happens During Pre-Training
You take a neural network with billions of parameters (Llama 3 has 405B). You feed it sequences of tokens. The model predicts the next token. You compute cross-entropy loss. You backpropagate. You repeat. For months.
Here's a simplified training loop in PyTorch-like pseudocode:
python
for epoch in range(100):
for batch in dataloader:
inputs, labels = batch
logits = model(inputs)
loss = cross_entropy(logits.view(-1, vocab_size), labels.view(-1))
loss.backward()
optimizer.step()
scheduler.step()
print(f"Epoch {epoch}, Loss {loss.item():.4f}")
Simple in theory. Nightmare in practice. At SIVARO we trained a 7B parameter model from scratch on a curated dataset of IoT documentation, sensor manuals, and technical schemas. It took 256 A100s running 24/7 for three weeks. The electricity bill alone was $180,000.
The Data Is Everything
Most people think pre-training is about architecture and compute. It's not. It's about data quality.
We tested two versions of our IoT model: one trained on raw web crawl data and one trained on a cleaned, deduplicated, domain-filtered dataset. The cleaned version — with 30% fewer tokens — achieved 40% lower perplexity on our target benchmarks.
The Talk with the Things paper from early 2025 shows similar findings: when integrating LLMs into IoT networks, the pre-training data distribution matters more than model size. They used a modified RoBERTa architecture pre-trained on 60 million IoT-related documents. Their model outperformed Llama 2 7B on device control tasks despite being 60% smaller.
If you're pre-training for a specific domain (IoT, healthcare, legal), you must curate your own data. Generic internet text doesn't cut it.
The Objective Functions You Actually See
Next-token prediction is the standard, but there are variants:
- Causal LM (GPT-style): predict next token, left-to-right. Best for generation.
- Masked LM (BERT-style): predict masked tokens, bidirectional. Best for understanding.
- Prefix LM (T5-style): encode prefix, decode autoregressively.
Which one should you pick? We ran a comparison experiment for IoT intent classification. BERT-style pre-training gave us 3% better accuracy on slot-filling tasks, but GPT-style was 5x faster at inference because we could cache KV vectors. Depends on your use case.
When You Should (and Shouldn't) Pre-Train
Let me be blunt: don't pre-train unless you have to.
Pre-training from scratch costs millions. Even with open-source frameworks like Megatron-LM or NeMo, you need a team of infra engineers who understand distributed training, checkpointing, and failure recovery. At SIVARO, we lost three training runs to GPU failures before we got it right.
If your task is narrow — classify sensor readings, generate simple commands — take an existing base model (Llama, Mistral, Gemma) and fine-tune it. Pre-training is only worth it if:
- You need a model that doesn't inherit biases from the web (e.g., medical diagnosis)
- You're targeting a highly specialized domain with its own syntax (e.g., code, legal contracts, IoT command languages)
- You want to own the model outright for regulatory reasons
The advancing IoT with generative AI and large language models article from 2024 makes this point: most IoT applications don't need a 400B parameter model. A fine-tuned smaller model runs on edge devices and responds in under 50ms.
Post-Training: Fine-Tuning, Instruction Tuning, and Alignment
This is the second type of LLM training — the part that turns a raw base model into something useful, safe, and controllable.
Post-training encompasses everything after pre-training: supervised fine-tuning (SFT), instruction tuning, reinforcement learning from human feedback (RLHF), direct preference optimization (DPO), and any other alignment technique. This is where you inject domain knowledge, style, safety constraints, and task-specific behavior.
Supervised Fine-Tuning (SFT)
You take the base model. You collect a dataset of input-output pairs — user queries and ideal responses, or sensor commands and expected actions. Then you train the model to mimic those outputs.
Here's what that looks like with Hugging Face Transformers:
python
from transformers import AutoModelForCausalLM, TrainingArguments, Trainer
model = AutoModelForCausalLM.from_pretrained("llama-3-7b-base")
tokenizer = ...
training_args = TrainingArguments(
output_dir="./sft-iot",
per_device_train_batch_size=4,
gradient_accumulation_steps=8,
learning_rate=2e-5,
num_train_epochs=3,
logging_steps=10,
save_strategy="epoch",
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset,
tokenizer=tokenizer,
)
trainer.train()
This works. But it has a gotcha: if your SFT dataset is small (less than 1,000 examples), the model will overfit and lose its generalization ability. We saw this in early 2023 when a client gave us 200 examples for a text-to-SQL task. The model memorized those 200 and failed on unseen queries. Solution: use LoRA (Low-Rank Adaptation) to restrict the number of trainable parameters.
LoRA freezes the base model and inserts small trainable matrices. You can fine-tune with 50 examples and not overfit.
python
from peft import LoraConfig, get_peft_model
lora_config = LoraConfig(
r=16,
lora_alpha=32,
target_modules=["q_proj", "v_proj"],
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
model = get_peft_model(base_model, lora_config)
We used LoRA to adapt a 7B model to IoT device control commands. 1,500 examples. Training on a single RTX 4090 took 4 hours. Inference added 2ms latency — negligible. The model went from 68% accuracy to 94%.
Instruction Tuning: Teaching the Model to Follow Directions
SFT teaches the model to answer questions. Instruction tuning teaches it to follow varied instructions — a superset of SFT.
The key insight: humans express the same request in many ways. "Turn off the lights", "Switch the living room lights off", "Kill the luminescence in the den". Instruction tuning datasets contain hundreds of thousands of prompt-response pairs covering diverse phrasings.
Google's Flan collection has 1,830 tasks. Alpaca from Stanford has 52,000 GPT-3.5-generated instruction-following examples. The LLM-Enhanced Security Framework for IoT Network paper (2025) showed that instruction-tuning a base Llama-2 model on IoT-specific security queries improved threat detection accuracy from 81% to 96%. Why? Because the model learned to interpret ambiguous commands like "Check the network" as "Perform an anomaly detection scan on all connected devices."
Alignment: RLHF and DPO
This is the most debated part of LLM training. Alignment ensures the model's behavior matches human values: helpful, harmless, honest.
RLHF (used for ChatGPT) requires a reward model trained on human preference data. Then you use PPO to update the LLM's policy. It's expensive and unstable. We tried it at SIVARO for a customer-facing chatbot. The training took 3 weeks and the reward model kept over-optimizing for politeness, making the agent refuse perfectly valid IoT commands like "Disable the temperature alerts." We had to roll back.
DPO (Direct Preference Optimization) is simpler: no reward model. You directly train the LLM on pairs of preferred and rejected responses. The LLM Agents for Internet of Things (IoT) Applications paper (2025) used DPO to align an IoT agent that controls home appliances. They achieved zero refusals on legitimate commands while blocking dangerous instructions like "Override the fire alarm."
DPO is easier to tune. We use it by default now.
The Critical Trade-Off: Generalization vs Specialization
Post-training inevitably degrades the base model's general capabilities if you go too far. This is called "catastrophic forgetting."
At first I thought this was a data quality problem — turns out it's an optimization problem. When you fine-tune on a narrow dataset, the model's weights drift away from the rich representations learned during pre-training. A model fine-tuned on IoT commands will forget how to write poetry.
Solutions:
- Multi-task fine-tuning: mix in general text during training
- EWC (Elastic Weight Consolidation): penalize weight changes that matter for old tasks
- Layer freezing: only update the last few transformer layers
We use layer freezing in production. For a 7B model, we freeze the first 20 layers and fine-tune the last 12. The model retains 90% of its general knowledge while learning the new domain. Understanding large language models: A comprehensive ... explains this trade-off well: "Fine-tuning trades versatility for precision."
So Which One Should You Invest In? (Practical Decision Framework)
Here's a matrix I've used at SIVARO for dozens of projects.
| Your situation | What to do |
|---|---|
| You have $50k and a narrow task (e.g., classify 10 IoT event types) | Take a small open-source model (Mistral 7B, Gemma 2B), fine-tune with LoRA |
| You have $500k and a moderately complex domain (e.g., IoT command parsing with edge deployment) | Pre-train a small model (1-3B) on domain data, then SFT |
| You have $5M+ and need a foundation model for your industry (e.g., medical, legal, industrial IoT) | Pre-train from scratch, then instruction-tune, then DPO align |
| You have $0 and want to start today | Download Llama 3 8B, run LoRA fine-tuning on a single GPU, deploy using vLLM |
Most companies don't need pre-training. The Leveraging Large Language Models for Autonomous ... paper from early 2026 analyzed 40 IoT startups. Only 2 had pre-trained their own models. The rest fine-tuned off-the-shelf models. And they achieved similar performance.
The real differentiator in production isn't training type — it's data quality and evaluation. The Large Language Models for Internet of Things systematic review found that "models fine-tuned on high-quality, expert-annotated datasets outperformed models that were 10x larger but trained on noisy data."
FAQ: Your Most Pressing Questions
Q: Do I need both pre-training and fine-tuning to get a working model?
A: No. 95% of production deployments use an open-source pre-trained model + fine-tuning. Pre-training from scratch is overkill for most applications.
Q: What's the difference between instruction tuning and RLHF?
A: Instruction tuning (SFT) teaches the model to follow instructions via direct mimicry. RLHF teaches it to prefer certain behaviors via a reward signal. RLHF gives better safety but harder to tune. DPO is a cheaper alternative.
Q: Can I fine-tune a model on my laptop?
A: For models up to 7B parameters, yes — if you use LoRA. A 7B model in 4-bit quantization with LoRA fits in 12GB VRAM. A single RTX 4090 can handle it. We've done it dozens of times.
Q: How much data do I need for fine-tuning?
A: For task-specific SFT, 500-5,000 examples is typical. For instruction tuning, aim for 10,000+. For alignment, 1,000 preference pairs can work with DPO. Quality matters more than quantity — one noisy example can degrade accuracy by 2-3%.
Q: What is "what are the two main types of llm training?" in the context of edge devices?
A: For edge deployment, you typically use pre-trained small models (1-3B) and fine-tune them. You cannot run pre-training on edge — it requires hundreds of GPUs. Post-training (fine-tuning) can be done on a single GPU and then quantized for edge inference. See Talk with the Things for a detailed edge deployment pipeline.
Q: Should I use supervised fine-tuning or RLHF?
A: Start with supervised fine-tuning. It's simpler and often sufficient. Add RLHF or DPO only if you see unsafe or unhelpful behaviors after SFT. We find DPO easier to implement than RLHF.
Q: Can I combine both types of training in one project?
A: Yes. Typical pipeline: pre-train a base model, then SFT on domain data, then DPO for alignment. This is what Meta did for Llama 3. It works. Just expect to spend 3-6 months and $500k+.
Conclusion
You asked what are the two main types of llm training. Here's the answer I've landed on after building production AI systems for 5+ years:
Pre-training builds the foundation — a model that knows language. Post-training (fine-tuning, instruction tuning, alignment) shapes that foundation into something useful, safe, and deployable.
Most people spend their energy on pre-training because it sounds more impressive. "We trained our own foundation model" gets headlines. But the real leverage is in post-training. A well-aligned 7B model beats a poorly-post-trained 70B model every time.
At SIVARO, we learned this the hard way in 2023 when we tried to launch a pre-trained-only IoT model. It could generate perfectly grammatical sensor descriptions but couldn't understand "turn off the AC" in an emergency. We scrapped the model, fine-tuned on 3,000 real IoT command logs, and hit 98% intent accuracy.
Today, July 23, 2026, the landscape is clearer than ever. Pre-training is a commodity — open-source models keep getting better (Llama 4 dropped two weeks ago). Post-training is where the competitive moat lives.
So stop worrying about scaling laws. Start curating your data, building your evaluation sets, and iterating on instruction tuning. That's where the actual work — and actual results — live.
Nishaant Dixit — Founder of SIVARO. Building data infrastructure and production AI systems since 2018. Built systems processing 200K events/sec.