An Intro To Fine Tuning

Pre-Training: Laying the Foundation for Fine-Tuning

Modern AI models, especially large language models (LLMs), learn in two major phases: pre-training and fine-tuning. Pre-training is the initial learning step where a model is exposed to a vast corpus of text data (potentially billions of words) without explicit human guidance. In this unsupervised phase, the model absorbs general linguistic patterns, grammar, facts, and even some reasoning abilities from raw text. This broad training equips the model with a foundation of general knowledge and language understanding.

However, a pre-trained base model is often not immediately ready to perform specialized tasks or follow complex human instructions. Andrej Karpathy once noted, “Base models are not assistants. They just want to complete internet documents.” In other words, a pre-trained LLM will tend to continue any text prompt in a statistically likely way, rather than perform a specific job you want. This is where fine-tuning becomes crucial.

Pre-training gives the model capability, but fine-tuning is often needed to give it specific skill and alignment. Pre-training is extremely resource-intensive; it can cost millions of dollars in compute and is typically done once by AI labs to create a general model. Fine-tuning, in contrast, is a lighter-weight process applied later to customize the model. The heavy lifting (learning how language works) is already done during pre-training, so fine-tuning can be achieved with a much smaller dataset and less compute.

What is Fine-Tuning and Why Is It Important?

Fine-tuning is the process of taking a pre-trained model and further training it on a specific dataset or task to adjust its behavior. Essentially, fine-tuning turns a general-purpose model into a specialist.

For example, an LLM like GPT-3 can generate fluent text about many topics, but it might not use medical terminology correctly. By fine-tuning GPT-3 on a corpus of medical records and articles, one can create a model that speaks the language of healthcare professionals. This specialization makes the model far more accurate and useful within that domain.

Fine-tuning is important because it bridges the gap between what a model learns in general and what a specific application needs. By fine-tuning, organizations can leverage powerful pre-trained models and customize them to their own data, style, or requirements. This offers huge practical advantages: one can achieve cutting-edge performance on a task without the cost of collecting billions of examples or training a massive model from scratch.

Equally important, fine-tuning often improves a model’s safety, reliability, and alignment with human expectations. A base model trained on internet text might output irrelevant or even toxic content if prompted naively. Fine-tuning with carefully curated examples can train the model to follow user instructions more faithfully and avoid undesirable outputs.

In general, fine-tuning is a powerful tool to inject new knowledge or preferred behavior into an AI model, making it more effective and trustworthy for real-world use.

How the Fine-Tuning Process Works

Fine-tuning an LLM involves several steps that refine the model’s parameters on new data:

Select a Pre-Trained Model: Choose a suitable base model close to your needs.
Prepare the Fine-Tuning Dataset: Gather and curate examples that demonstrate desired behavior.
Preprocess and Tokenize: Convert text into tokenized format usable by the model.
Configure Training: Set learning rate, freeze/unfreeze layers, and choose objective functions.
Train the Model: Run training over several epochs to adjust weights.
Evaluate and Iterate: Use validation data to test and improve.
Deploy the Fine-Tuned Model: Use the adapted model in production.

Care must be taken not to overfit or “over-steer” the model. Catastrophic forgetting (where the model loses previously learned general knowledge) is a known issue, which techniques like regularization or mixed datasets aim to reduce.

Use Cases of Fine-Tuning

Domain-Specific Assistants: E.g. medical, legal, finance models.
Task-Specific Optimization: Sentiment analysis, summarization, translation.
Customer Support: Tailored chatbots with specific tone and procedures.
Safety and Alignment: Teaching models to follow instructions and avoid harmful content.
Style/Format Conversion: E.g. document-to-summary, JSON-to-text, brand-consistent writing.

Comparison of Adaptation Approaches: Prompting vs. Fine-Tuning vs. RAG

Fine-Tuning Techniques: SFT, RLHF, PPO, DPO, GRPO

Supervised Fine-Tuning (SFT): Train on input-output pairs. Simple, effective, but may miss nuanced preferences.
Reinforcement Learning with Human Feedback (RLHF): Use human-ranked outputs to train a reward model, then optimize model behavior to align with human preference.
Proximal Policy Optimization (PPO): The RL algorithm often used in RLHF.
Direct Preference Optimization (DPO): Simplifies RLHF by using pairwise preferences directly in training, without RL.
Group Relative Policy Optimization (GRPO): RL algorithm of Proximal Policy Optimization (PPO) that replaces the traditional value network by using the average reward of multiple sampled outputs as a baseline for more efficient and scalable fine-tuning.

Comparison: SFT vs. RLHF vs. PEFT

Parameter-Efficient Fine-Tuning (PEFT) Methods

Adapters: Insert small layers between frozen base layers.
LoRA (Low-Rank Adaptation): Learn low-rank updates to weights.
QLoRA: Combines LoRA with 4-bit quantization.
Prompt/Prefix Tuning: Learn vector embeddings prepended to inputs.

PEFT methods offer significant advantages in adapting large pre-trained models to specific tasks. By updating only a small subset of parameters, these methods drastically reduce the number of parameters that need to be trained, leading to more efficient use of memory and computational resources.

Additionally, the reduced parameter footprint simplifies model sharing and storage, as the smaller size facilitates easier distribution and deployment across various platforms. Beyond these benefits, PEFT methods often enhance the generalization capabilities of models, as they mitigate the risk of overfitting by focusing adjustments on task-relevant parameters while preserving the pre-trained knowledge. Furthermore, the modular nature of techniques like Adapters allows for the seamless integration of multiple tasks into a single model without significant interference, promoting versatility and scalability in real-world applications.

ChatGPT vs DeepSeek Finetuning

ChatGPT: ChatGPT is fine-tuned using a combination of SFT and RLHF via PPO. In SFT, the base model is trained on human-annotated prompt-response pairs to establish foundational behavior. Then, RLHF is applied, where a reward model is trained based on human preference rankings. This reward model guides further updates through PPO, a policy-gradient method that ensures stable optimization in high-dimensional spaces. This multi-stage pipeline allows ChatGPT to generate high-quality responses aligned with human expectations. It remains the industry standard for instruction-tuned LLMs and has been used across all GPT-3.5 and GPT-4 deployments.

DeepSeek R1: DeepSeek uses GRPO, a variant of policy optimization that avoids explicit reward modeling. Instead of relying on external preference labels to train a reward model, GRPO compares multiple outputs internally from a batch and optimizes the model by favoring the relatively better outputs. This approach is inspired by the idea that a model can learn from its own generations when structured comparisons are applied, reducing the need for costly human supervision. DeepSeek reports that GRPO yields more efficient fine-tuning, especially in reasoning-intensive tasks, and is more scalable for open-source training.

Challenges in Fine-Tuning

Data Bias: Garbage in, garbage out.
Overfitting & Forgetting: Must balance adaptation with generality.
Compute & Access Constraints: Especially with closed-source or large models.
Evaluation: Hard to judge performance on subjective or creative tasks.
Safety & Gaming in RL: Models may optimize reward in unintended ways.

Conclusion and Future Directions

Fine-tuning transforms generic AI models into task-specific specialists, enabling them to better align with real-world applications. As the adoption of LLMs continues to grow, fine-tuning methods are expected to evolve significantly. One major direction is multimodal fine-tuning, where models are trained to process and integrate multiple types of data such as text, images, video, and audio. Another area gaining momentum is continual learning, which allows models to be updated periodically without needing to retrain from scratch, crucial for keeping models current in dynamic environments. Additionally, auto-fine-tuning is an emerging trend, where models autonomously improve by generating synthetic data and leveraging their own reasoning capabilities to self-correct. Together, these advancements highlight that fine-tuning remains essential for making AI not only more intelligent but also more practical and adaptable in the real world.

References

Understanding and Using Supervised Fine-Tuning (Cameron R. Wolfe)

https://cameronrwolfe.substack.com/p/understanding-and-using-supervised

The Fine-Tuning Landscape in 2025: A Comprehensive Analysis (Pradeep Das)

https://medium.com/@pradeepdas/the-fine-tuning-landscape-in-2025-a-comprehensive-analysis-d650d24bed97

Fine-Tuning AI Models: A Guide (Prabhu S)

https://medium.com/@prabhuss73/fine-tuning-ai-models-a-guide-c515bcd4b580

Prompt Engineering vs Fine-Tuning vs RAG (MyScale Team)

https://medium.com/@myscale/prompt-engineering-vs-finetuning-vs-rag-cfae761c6d06

Easily Explained: RAG vs Fine-Tuning in LLMs (Nour Badr)

https://medium.com/@nour_badr/easily-explained-rag-vs-fine-tuning-in-llms-f5df5c5d6342

RAG vs Fine-Tuning: How to Choose the Right Method (Yugank Aman)