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.

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. 

What is supervised fine-tuning (SFT)?

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:

Step 1: Start with a pre-trained LLM

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.

Step 2: Curating a labeled dataset

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.

Step 3: Training via supervised learning

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:

• Forward pass: The LLM generates its own predicted output for each input in the dataset.
• Loss calculation: A loss function, commonly cross-entropy loss, is used to quantify the difference between the LLM's predicted output and the desired "ground truth" output from the labeled dataset. This loss represents how "wrong" the model was for that specific example.
• Back-propagation and optimization: The calculated loss adjusts the internal parameters (weights and biases) of the LLM through an optimization algorithm such as Adam. This adjustment is done via back-propagation, pushing the model to minimize the loss and produce outputs closer to the desired labeled outputs.
• Iteration: This cycle of forward pass, loss calculation, and back-propagation is repeated numerous times across the entire labeled dataset (over multiple epochs), iteratively refining the LLM's parameters.

Common use cases for supervised fine-tuning

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.

Customer support automation: Intelligent and brand-aligned chatbots

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:

• Provide company-specific answers: The fine-tuned LLM learns to use information and solutions relevant to the organization's products, services, and policies, and can provide summarizations of them on demand.
• Maintain brand voice and tone: The training data reflects the desired brand personality, ensuring that the chatbot communicates consistently and appropriately.
• Effectively handle complex inquiries: Chatbots trained with SFT using high-quality training data can understand complex questions and engage in multi-turn conversations effectively.

Medical text analysis: Assisting healthcare professionals

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:

• Analyzing medical literature: Fine-tuned LLMs can help clinical experts process and summarize large volumes of medical literature to ensure that they remain updated on the latest findings.
• Extracting key information from patient records: SFT enables models to efficiently extract critical information from electronic health records, assisting in diagnosis, treatment planning, and risk assessment.
• Improving diagnostic assistance: SFT fine-tuned LLMs can serve as valuable diagnostic support tools by analyzing patient symptoms and medical history, and providing potential diagnoses for clinicians to consider. For example, ChatDoctor (a chat model fine-tuned in the medical domain) enhances diagnostic accuracy and efficiency, helping healthcare providers to make more informed decisions.

The benefits and limitations of supervised fine-tuning

When considering SFT for your LLM training strategy, these benefits stand out:

Benefits of SFT

• Simplicity and ease of use: SFT is straightforward, with a training process and objectives conceptually similar to pre-training.
• Effectiveness: It is highly effective for alignment, improving instruction-following capabilities, correctness, coherence, and overall performance of language models. Models can generate dialogue sessions similar to those written by humans after SFT.
• Computational efficiency: SFT is computationally inexpensive, requiring significantly less computational power than pre-training.
• Democratizes AI access: This reduces dependency on large labeled datasets, making advanced AI solutions feasible in low-resource environments.
• Provides static safeguards: Static safeguards through carefully curated datasets that reflect societal norms and values.

Limitations of SFT

Despite its strengths, SFT also has limitations that are important to consider when choosing an LLM training approach. 

• High cost and time for data curation: Creating large, high-quality labeled datasets can be expensive and time-consuming, particularly in specialized domains like healthcare, law, or finance, where expert annotation is required.
• Overfitting: LLMs fine-tuned with SFT are prone to overfitting, especially when trained on limited or biased datasets. This leads to poor generalization in real-world scenarios.
• Static nature: Once fine-tuned, SFT models are static and cannot adapt to changing requirements without retraining on updated datasets. This limitation reduces the model’s relevance in dynamic environments, such as regulatory compliance systems adapting to new laws.
• Limited performance in ambiguous tasks: SFT models struggle with tasks requiring multi-step reasoning, creativity, or dynamic adaptation. Predefined input-output mappings fail to address the complexity of subjective tasks such as creative writing or ethical decision-making.
• Ethical alignment: Ethical alignment in SFT relies on the quality of the labeled datasets. Biases present in the training data can unintentionally propagate into the model’s outputs.

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. 

What is RLHF?

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.

Step 1: Initial supervised fine-tuning

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.

Step 2: Human guidance collection

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.

Step 3: Reward model training

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.

Step 4: Fine-tune with reinforcement learning

After training the reward model on human preferences, the final RLHF step applies reinforcement learning to further fine-tune the policy model. This involves:

• Policy model response generation: The policy model generates new responses.
• Reward scoring or reward signal by the reward model: The trained reward model evaluates these generated responses and assigns a reward score to each.
• Reinforcement learning optimization: A reinforcement learning algorithm, such as Proximal Policy Optimization (PPO), updates the policy model's parameters. The algorithm's objective is to adjust the policy model to maximize the reward scores predicted by the reward model. Essentially, the policy model is "reinforced" to produce responses that the reward model would consider high quality.
• Iteration and refinement: RLHF is often an iterative process. Human feedback is collected on its new, improved responses as the policy model improves. The reward model might be further refined, and the policy model might be fine-tuned again using reinforcement learning. This iterative feedback loop is key to continuous improvement and alignment with evolving human preferences.

Common use cases of RLHF

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.

AI-powered content moderation: Aligning with evolving human values and policies

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:

• Dynamically adapt to policy changes: RLHF enables the iterative refinement of models based on human feedback. This process adapts to changing community standards and platform policies, ensuring ongoing alignment with current guidelines.
• Handle complex content judgments: Human moderators give feedback on subtle violations and contextual issues. RLHF helps models understand nuances and improve their judgment in complex moderation.
• Reduce bias and improve fairness: RLHF can help reduce biases in static moderation datasets or rules by incorporating diverse human feedback. 

Conversational AI refinement: Creating engaging and user-centric dialogues

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:

• Optimize for user engagement and satisfaction: Human feedback can help models generate accurate, engaging, and polite responses suited to user needs and maximize satisfaction.
• Adapt to user preferences: RLHF helps chatbots learn from user interactions, adjusting their style and responses to better match user preferences over time. 
• Enhance naturalness and flow of dialogue: RLHF creates chatbots for more natural, human-like conversations by integrating human feedback on dialogue flow, avoiding robotic interactions.

The benefits and limitations of RLHF

RLHF has many advantages compared to SFT, mainly when dealing with human preferences and ethical AI alignment. Some of its benefits are listed below.

Benefits of RLHF

• Improved performance: RLHF incorporates human feedback into the learning process, enabling AI systems to understand complex human preferences better and generate more accurate, coherent, and contextually relevant responses.
• Reduced biases: Through an iterative feedback process, RLHF helps to identify and mitigate biases present in the initial training data, ensuring alignment with human values and minimizing undesirable biases.
• Continuous improvement: RLHF facilitates ongoing improvement in model performance. As trainers provide more feedback and the model undergoes reinforcement learning, it becomes increasingly proficient in generating high-quality outputs.
• Adaptability: RLHF allows AI models to adapt to different tasks and scenarios by using human trainers’ diverse experiences and expertise. This enables the models to excel in various applications, including conversational AI and content generation.
• User-centric design: By incorporating human feedback, RLHF allows AI systems to better understand user needs, preferences, and intentions. This leads to more personalized and engaging experiences as the models generate responses tailored to individual users.

Limitations of RLHF

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.

• Hallucinations and bias: Models trained using RLHF can suffer from issues such as hallucinations and bias. The reward model might overfit to patterns in human feedback that unintentionally reinforce flawed or biased responses, particularly if the feedback dataset lacks diversity or contains subjective biases.
• Adversarial attacks: RLHF-trained models are also susceptible to adversarial attacks, including unusual jailbreaks that can trick them into bypassing their safeguards.
• Misleading humans: RLHF can mislead humans during the annotation process. Language models trained with RLHF can sound confident even when they are incorrect, which can lead humans to provide more positive feedback.
• Subjectivity and human error: The quality of feedback can vary among users and testers. Finding experts to provide feedback can be time-consuming and challenging without a reliable AI training partner.
• Wording of questions: The quality of an AI tool's answers depends on the queries. The LLM might not fully understand the request if a question is too open-ended or the user's instructions are unclear.

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. 

When to use supervised fine-tuning vs. RLHF

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.

Best practices for data preparation

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.

Supervised fine-tuning best practices

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:

• Prioritize relevance and quality data: Gather data that mirrors your target task, such as customer support logs for a chatbot or medical records for diagnostics. Quality, domain-specific examples ensure that the model learns relevant features and patterns.
• Expert annotation: Employ human experts with experience in your domain and use case to label input-output pairs accurately. Clear, consistent annotations align the model with task expectations.
• Dataset curation: Curate a dataset large enough to cover key scenarios but diverse enough to prevent overfitting.
• Evaluation with benchmarks: After fine-tuning LLMs with SFT, evaluate performance using task-specific benchmarks like accuracy for classification and BLEU scores for translation.

RLHF best practices

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:

• Develop reward models: Train human evaluators to rank outputs consistently using clear guidelines. A well-defined scoring system ensures that the reward model reflects valid preferences. An experienced AI training partner will be able to provide your AI team with both the guidelines and expert human evaluators quickly, preventing any further delays for your AI initiative without compromising on quality.
• Establish feedback loops: Set clear metrics for feedback and gather rankings from various outputs. Consistent feedback loops enhance the model gradually.
• Scalability: Prioritize high-impact examples for human review using active learning techniques. This reduces the volume of feedback needed while maximizing improvement.
• Ethical oversight: Feedback and outputs should be regularly audited to ensure alignment with ethical standards, such as fairness and safety.

The key to choosing the right training method? AI expertise 

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.

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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. 

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Glossary: AI training terms you need to know

Active learning

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

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

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

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

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.

Reinforcement learning from human feedback (RLHF)

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.

Reward model

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.

Supervised fine-tuning (SFT)

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.

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