Large Model Pretraining and Fine‑Tuning: A 2026 Technical Guide from Scaling Laws to Post‑Training Revolution

1. Pretraining: the “nine‑year compulsory education” of large models

1.1 What pretraining is

Pretraining lets a model learn to "speak" by ingesting massive unlabeled data (webpages, books, code, dialogues) and discovering linguistic patterns, word relations, sentence structures, and world knowledge without any task‑specific supervision.

1.2 Scaling Law: bigger is stronger?

Following the 2020 Scaling Law paper, OpenAI showed that model performance predictsably improves with larger parameter count (FLOPs), more data, and more compute, enabling breakthroughs such as GPT‑3 (175 B), GPT‑4, and projected trillion‑parameter clusters for 2026.

1.3 Chinchilla law: data matters as much as parameters

DeepMind’s 2022 Chinchilla paper added a crucial correction: model size and data volume must grow together; otherwise compute is wasted. Training a 70 B model with more data outperformed GPT‑3 (175 B), highlighting the importance of data engineering over blind parameter scaling.

1.4 Is Scaling Law hitting a wall in 2026?

The “wall” is not the law itself but "brute‑force scaling"—simply adding parameters and data yields diminishing returns. New scaling dimensions are emerging:

Test‑time Compute Scaling : increase inference compute (e.g., OpenAI o1/o3) instead of only training compute.

Data‑quality Scaling : supplement scarce real data with synthetic data generated by models (DeepSeek‑R1 demonstrates feasibility).

Post‑training Scaling : continue to "activate" capabilities after the base model is trained using reinforcement learning.

Thus scaling now targets the full chain of training + inference + post‑training.

1.5 Core challenge: data

By 2026, high‑quality language data is being consumed at an unprecedented rate. Major labs respond with:

DeepSeek‑R1 : uses chain‑of‑thought (CoT) synthetic data, letting the model generate reasoning steps that become training data for the next generation.

GPT‑5 : adopts multimodal pretraining, jointly ingesting vision, audio, and code data to broaden data sources.

Llama 4 : applies aggressive data filtering and quality scoring to extract valuable content from massive web crawls.

Data‑quality engineering has become the decisive factor for pretraining success.

2. Fine‑tuning: from generalist to specialist

2.1 What fine‑tuning is

The pretrained model is a "generalist"—it knows a little about everything but excels at nothing. Fine‑tuning uses labeled task‑specific data to adapt the model to concrete scenarios such as customer‑service dialogue, code generation, medical QA, or legal drafting.

2.2 Full‑parameter vs parameter‑efficient fine‑tuning

Full‑parameter fine‑tuning updates every weight, delivering the best performance but requiring massive compute and data, which most teams cannot afford.

Parameter‑efficient fine‑tuning (PEFT) updates only a small subset of parameters while keeping the backbone frozen, dramatically lowering cost and becoming the dominant approach.

2.3 LoRA (Low‑Rank Adaptation)

LoRA decomposes the update into low‑rank matrices, training only the added parameters. The analogy used is: the pretrained model is a building’s frame; LoRA swaps out the furniture in a single room without rebuilding the whole structure.

3. Supervised Fine‑tuning (SFT)

3.1 Definition

SFT fine‑tunes on manually labeled "question‑answer" pairs, letting the model imitate correct responses. It is simple, stable, and the foundation for all later alignment steps.

3.2 Limitations

SFT can only teach the model to copy correct answers; it cannot convey the notion of "rightness". Consequently, when faced with unseen situations, SFT‑trained models may produce confident but nonsensical outputs, motivating the need for alignment techniques.

4. RLHF era: making models "understand" human preferences

4.1 Why RLHF

Pretraining teaches "how to speak"; SFT teaches "what correct words are". However, style, safety, and usefulness still depend on human judgment, which RLHF injects.

4.2 Three‑step RLHF pipeline

Step 1: SFT – fine‑tune on labeled data.

Step 2: Train a Reward Model – humans label preferred vs. non‑preferred model outputs; the reward model learns to predict these preferences.

Step 3: PPO reinforcement learning – the reward model guides policy optimization so the model generates outputs humans like.

This pipeline underlies GPT‑4, Claude, GPT‑5, and similar models.

4.3 Cost of PPO

PPO delivers strong results but is extremely expensive:

Reward model training requires a separate model roughly half the size of the base model.

Critic model doubles VRAM consumption.

Every training step needs fresh human preference data, driving up annotation costs.

By 2026, with trillion‑parameter models, PPO costs have become prohibitive for most teams.

5. Direct Preference Optimization (DPO)

5.1 What DPO does

Proposed by Stanford in 2023, DPO skips the reward model and PPO, directly optimizing the policy with "chosen vs. rejected" preference pairs.

No separate reward model.

No PPO‑style exploration‑exploitation trade‑off.

Lower memory footprint (no extra critic).

5.2 Limitations

DPO is still a form of contrastive learning, not true reinforcement learning.

Performance on complex reasoning tasks lags behind RLHF.

Highly sensitive to the quality and distribution of preference data.

6. Post‑training revolution in 2026

6.1 Why RLHF/DPO are being replaced

Three bottlenecks of RLHF become untenable at scale:

Critic model doubles VRAM, a fatal issue for trillion‑parameter models.

Human annotation requires millions of preference labels per training run.

Scalability suffers because human labeling cannot keep pace with model iteration.

These pressures gave rise to three alternative routes: GRPO, DAPO, and RLVR.

6.2 GRPO (Group Relative Policy Optimization)

DeepSeek‑R1 introduced GRPO, which replaces the critic with intra‑group ranking of sampled responses. For each query, 8–64 answers are generated, ranked relative to each other, and the ranking is turned into advantage values. No separate critic is trained, halving VRAM usage.

Benchmark (AIME 2024) shows:

DeepSeek‑R1‑Zero with PPO: 71.0 score, ~10K training steps, baseline VRAM.

DeepSeek‑R1 with GRPO: 79.8 score, ~8K steps, VRAM reduced by 50%.

6.3 DAPO (Dynamic Advantage Policy Optimization)

Designed for long‑chain reasoning (math, code), DAPO solves two core issues:

Token‑level gradient vanishing : broadcasts advantage values to every token, preserving gradient signal across long sequences.

Entropy collapse : a Clip‑Higher technique raises the policy‑ratio ceiling, maintaining exploration diversity late in training.

Experiment: Qwen2.5‑32B + DAPO reaches 50 points on AIME 2024 after only 5 K steps—50 % fewer steps than PPO—while the open‑source implementation remains stable.

6.4 RLVR (Reinforcement Learning with Verifiable Rewards)

RLVR eliminates human labeling by using automatic verifiers as reward signals. For math problems the verifier checks the final answer; for code it runs unit tests; for logic it validates derivations. Rewards are binary (1 = correct, 0 = incorrect), making them simple, reliable, and scalable.

DeepSeek‑R1 experiments reveal that pure RLVR training can induce self‑reflection and dynamic strategy switching without any human‑generated chain‑of‑thought data.

Training cost: $294,000, which is orders of magnitude lower than the annotation‑heavy RLHF pipelines.

7. Comparison of the three post‑training methods

GRPO : replaces critic with group ranking; suitable for general LLM alignment (>10 B); low VRAM; requires multi‑response sampling.

DAPO : token‑level advantage + dynamic sampling; excels at long‑chain tasks (math, code); low VRAM; needs long‑sequence data.

RLVR : verifiable rewards replace human labels; ideal for tasks with automatic correctness checks; extremely low VRAM; no human data needed.

8. Full training pipeline for 2026

Pretraining (Scaling Law + data engineering) ↓ SFT (labeled data) ↓ Post‑training (GRPO/DAPO/RLVR) ↓ Deployment

9. Practical advice for individuals and teams

Use off‑the‑shelf pretrained models (Llama, Qwen, DeepSeek) for most scenarios; fine‑tuning is optional.

Prefer LoRA for parameter‑efficient fine‑tuning; it offers low cost and strong performance.

For alignment, choose DPO for general dialogue and RLVR for math/code where verifiable rewards exist.

Prioritize data quality: cleaning 1 000 high‑quality examples beats using 10 000 noisy ones.

References

DeepSeek‑R1 paper (2026‑01), ARC Prize Foundation, llm‑stats.com 2026‑03 review, Zhiyuan Institute 2026 report; OpenAI GPT‑5 technical report; Anthropic Claude training whitepaper; DeepSeek technical blog.