Generic LLMs like GPT, LLama, and Gemini are powerful and broad — but they often lack the accuracy and contextual information in specialized fields such as healthcare, law, or finance to meet the needs of domain-specific applications like chatbots and AI assistants. In these cases, generic models may produce incorrect, biased, or ambiguous outputs, leading to costly errors.
To train an LLM or foundation model from its original, broad understanding to work in specific use cases, AI teams will need to fine tune them first. Fine-tuning techniques has become crucial for customizing large language models (LLMs), as this process enables models to comply with domain-specific standards, improves reliability, and builds trust among users. Among various fine-tuning methods, supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) stand out for their success in delivering high quality fine-tuned models.
SFT relies on labeled datasets and excels with well-defined tasks, while RLHF uses reward-driven learning to optimize models for real-time adaptability, making it ideal for complex tasks.
Choosing the right fine-tuning method is critical for operational efficiency and maximizing return on investment (ROI). In this article, we’ll explore how these techniques work, along with their strengths, limitations, and applicable use cases. We will also discuss the trade-offs between the two methods to assist AI teams implementing LLMs in real-world scenarios.
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Supervised fine-tuning is a method to adapt a pre-trained model to a specific task by further training it on a task-specific dataset. It uses high-quality labeled datasets containing input-output pairs curated by human experts to optimize the LLM's parameters and improve accuracy.
LLMs already have rich, generalized language representations from unsupervised pre-training. With SFT, they learn to perform specialized tasks.
The process of fine-tuning an LLM with SFT usually looks like this:
The starting point for SFT is always a pre-trained model that has already learned general language representations from a massive corpus of text — often referred to as a base model. This gives it a broad understanding of grammar, context, meaning, and even world knowledge for natural language processing (NLP). SFT uses this pre-existing linguistic competence, building upon a solid foundation rather than starting from scratch.
The success of SFT is highly correlated to the quality of the training data, so AI teams will typically create a curated, high quality labeled dataset consisting of input-output pairs relevant to the requirements of the target task.
For instance, if the goal is sentiment analysis, the dataset might include phrases like, “I loved this movie” (input) paired with a “positive” (output). For a medical use case, this could involve doctor-patient dialogues labeled with correct diagnoses.
The curated SFT dataset typically has a much smaller batch size than a pre-training dataset.
The pre-trained LLM undergoes further training during the SFT stage, but now specifically on the curated labeled dataset. This training process uses the principles of supervised learning. Below is a simplified view:
Supervised fine-tuning is used across many applications where tasks are well-defined, rule-based, and require domain expertise. Below are some of the most common and impactful use cases where SFT drives efficiency and accuracy.
In customer service, consistent, accurate, and brand-aligned communication is essential. SFT helps create intelligent chatbots that offer more than just generic responses. Businesses can develop these chatbots by fine-tuning LLMs with a curated dataset of previous customer service transcripts, FAQs, and articles from the company knowledge base. It can:
The medical field involves highly specialized language and requires the highest accuracy. SFT is used in medicine to adapt LLMs for healthcare applications. Training an LLM on expert-annotated medical texts, research papers, clinical guidelines, and patient records (with appropriate privacy safeguards), SFT can create models capable of:
When considering SFT for your LLM training strategy, these benefits stand out:
Despite its strengths, SFT also has limitations that are important to consider when choosing an LLM training approach.
Invisible’s team of AI experts has trained and fine-tuned 80% of the leading foundation models. Put our experience to use — if you’re ready to get your AI application deployment-ready quickly and efficiently, request a demo today.
Reinforcement learning from human feedback (RLHF) fine-tunes a pre-trained LLM by incorporating human feedback as a reward function during the training process. Human evaluators assess the model’s outputs, ranking them based on quality, relevance, or alignment with specific goals, and this feedback trains a reward model.
RLHF training optimizes the LLM to maximize the rewards predicted by this model, effectively adjusting its behavior to better align with human preferences.
The RLHF process is iterative and feedback-driven, refining the LLM’s outputs through a structured sequence of steps. Here’s how it works.
The RLHF process usually starts with SFT. In the initial phase, AI teams train a "policy" model, which serves as the LLM for further refinement through RLHF. This provides the model with a solid starting point for both general language understanding and some task-relevant skills.
Human evaluators review the policy model generated text, providing feedback by ranking them, giving detailed critiques, or labeling them as either ‘good’ or ‘bad’. This human input is important for constructing a reward model that can understand human preferences.
A separate model, the reward model, is then trained to predict human preferences. The training data for the reward model consists of the model-generated responses paired with the human rankings or ratings. The reward model aims to learn to assign a numerical score to each response, such that responses that humans preferred receive higher reward scores and less preferred responses receive lower scores.
Using a reward model to provide reinforcement can also be referred to as RLAIF, or reinforcement learning with AI feedback.
After training the reward model on human preferences, the final RLHF step applies reinforcement learning to further fine-tune the policy model. This involves:
While SFT works well with tasks with clear input-output mappings, RLHF comes into its own when handling more subjective goals, ethical guidelines, and real-world feedback. Here are some key use cases for RLHF.
Content moderation is challenging, and AI systems must adapt to changing norms and content policies. RLHF provides a way to train AI models for content moderation and go beyond static rule-based systems. Content platforms can enhance moderation tools by fine-tuning LLMs using RLHF, so that they:
Simply generating factually correct responses is often not enough in conversational AI. Users expect chatbots to be engaging, natural-sounding, and aligned with their preferences. RLHF is instrumental in conversational AI refinement, allowing for the creation of chatbots that:
RLHF has many advantages compared to SFT, mainly when dealing with human preferences and ethical AI alignment. Some of its benefits are listed below.
In addition to RLHF advantages, it's important to recognize its limitations. These drawbacks primarily arise from the complexity of the RLHF process and its reliance on subjective human feedback. Some key limitations of RLHF are listed below.
Invisible’s team of AI experts has trained and fine-tuned 80% of the leading foundation models. Put our experience to use — if you’re ready to get your AI application deployment-ready quickly and efficiently, request a demo today.
Choosing between SFT and RLHF depends on your project’s specific goals, resources, and task requirements. SFT is best with an optimal, labeled dataset and clear tasks, while RLHF is ideal for dynamic adaptation and aligning with human judgment. The choice ultimately depends on the needs of your application and available resources. However, expert guidance can help you avoid costly errors by helping you choose a method that aligns with your resources and objectives. This prevents missteps such as over-investing in compute for RLHF or using SFT for tasks that require dynamic adaptation.
Now, let's understand the best practices for preparing your data to maximize your fine-tuning success.
Effective LLM fine-tuning depends on the quality of your data preparation. How you gather, refine, and structure your data directly impacts the model’s performance. Below are the best practices for each approach.
For supervised fine-tuning LLMs, focus on creating a high-quality, task-specific dataset that guides the model toward precise, reliable outputs. Here’s how:
RLHF relies on iterative human feedback instead of static datasets, so preparation focuses on consistent, scalable evaluations. Here’s how you can enhance the RLHF data processes:
When choosing between SFT and RLHF, consider the task and resources. Both methods are potent for refining LLMs. However, their success depends on careful implementation, and any missteps can delay AI projects and reduce ROI. Expert guidance can enhance the advantages of these approaches and contribute to success in several key ways.
With expert guidance, you can build strong data curation and annotation processes that produce high-quality, domain-specific training datasets, enhancing your LLMs' performance. Their involvement streamlines the entire fine-tuning process, from hyperparameter optimization to deployment. This minimizes the need for costly, time-consuming trial-and-error, enabling you to get your LLM deployment-ready fast.
Invisible’s team of AI experts has trained and fine-tuned 80% of the leading foundation models. Put our experience to use — if you’re ready to get your AI application deployment-ready quickly and efficiently, request a demo today.
Active learning is a data-efficient technique where the LLM iteratively identifies and prioritizes unlabeled data for human annotation. This reduces labeling costs while improving model performance, particularly in RLHF and scenarios with scarce training data.
Annotation refers to labeling or tagging data, such as text, images, or audio, with information that an AI model can use for training. Human experts annotate datasets with correct answers in SFT to teach the model specific tasks.
Fine-tuning LLM refers to adjusting a pre-trained LLM to improve its performance on a particular task or domain. Unlike initial training on vast, general data, fine-tuning uses smaller, targeted datasets to specialize the model’s capabilities. Fine-tuning transforms a general-purpose LLM into a powerful, customized tool for real-world applications.
Machine learning is a subset of artificial intelligence that enables computers to learn from data and improve performance on tasks without explicit programming. It involves algorithms that identify patterns, make predictions, and adapt over time. Common techniques include supervised, unsupervised, and reinforcement learning, applied in fields like healthcare, finance, and automation.
Overfitting happens when an AI model learns a training dataset too well, including its noise or unique characteristics, and then struggles to perform effectively on new, unseen data. This can occur during the fine-tuning of LLMs if the dataset is too small or lacks diversity, resulting in poor generalization.
RLHF is a fine-tuning approach that improves LLMs by using human evaluations to guide learning. Instead of fixed labels, humans rank the model’s outputs. A reward model reflects these preferences, which the LLM optimizes through reinforcement learning. It enhances an AI’s ability to produce complex, context-appropriate responses.
A reward model is a component of RLHF trained to evaluate and score LLM outputs based on human preferences. It quantifies the quality of responses (e.g., “How helpful was this answer?”) and guides the LLM to optimize for desired outcomes. Reward models can handle tasks requiring nuanced judgment, such as conversational AI or safety-critical systems.
SFT refines a pre-trained LLM on a labeled dataset specific to a task. In SFT, human experts provide input-output pairs, such as questions and answers, to guide the model toward accurate, task-focused responses.
This technique enhances an LLM’s performance in customer support or medical text analysis applications, where precision and consistency are crucial. SFT builds on the model's existing knowledge to make AI more dependable and customized to specific needs.
