Key Takeaways

• RLHF uses human feedback to make large language models sound more accurate, helpful, and natural.
• The process starts with human-created prompts and supervised fine-tuning of model responses.
• A reward model then helps the AI evaluate and improve its own outputs over time.
• RLHF is used beyond text, including in robotics, games, and other generative AI systems.
• While powerful, RLHF is limited by the subjectivity and potential bias of human feedback.

Vivek Shah is a Los Angeles-based entrepreneur focused on building technology platforms that address complex, real-world problems. Vivek Shah has founded and led multiple startups spanning consumer marketplaces, mobile applications, and artificial intelligence–driven systems. His ventures include Gowd, a subscription-sharing platform, Strance, a livestream mobile app for short-form talent discovery, and DriverChatter, an integrated communication platform for rideshare drivers. He currently serves as CEO of Gauge AI, where he works on integrating AI systems into business workflows while also directing investments across several industries.

Beyond entrepreneurship, Vivek Shah is active in community engagement as the founder of Los Angeles Hope for Kids, an organization dedicated to outreach, mentorship, and educational support. His professional background, which includes experience in technology sales and AI deployment, informs a practical interest in how modern machine learning techniques operate. This perspective aligns with topics such as reinforcement learning from human feedback, a method increasingly used to train large language models to produce more accurate, context-aware, and human-aligned responses.

How Does RLHF Work for LLM Training?

Artificial intelligence (AI) has become increasingly prevalent in society in recent years. Thanks in part to advances in technology and training methods, the AI market is now worth hundreds of billions of dollars and the technology has a vast range of applications, including predicting stock performance, supporting medical decisions, and enabling self-driving cars. Different training techniques, including reinforcement learning from human feedback (RLHF), help AI models better reflect human nature in their responses and behaviors.

RLHF is primarily used to improve large language models (LLMs), particularly in their ability to perform intricate tasks with goals that don’t involve mathematics. For example, an LLM may not fully understand the concept of humor solely through human-generated text. Through RLHF, it can enhance its joke writing abilities via human evaluation of its responses. RLHF also helps machine learning (ML) models, like chatbots, sound more natural and provide appropriate context.

In its simplest form, RLHF works by humans analyzing and comparing an AI model’s responses to prompts with human responses. This could be anything such as asking it about the weather or the tone of a movie. The human grades each of the model’s responses on accuracy and innately human qualities, including friendliness. Ultimately, it is a process of trial and error in which model responses are refined and made to appear more human over time.

Data collection is the first of the four stages of RLHF. In this stage, a human creates a series of prompts and responses as training data for the AI model. For instance, an employee might produce questions and answers about their company’s finances or policies. Then, during the supervised fine-tuning stage, humans can assign scores based on the degree of accuracy to the machine-generated responses. These scores help establish a policy that informs all future responses.

The third stage of RLHF involves creating a separate reward model to optimize the model’s policy. The human produces a scalar reward value output that helps the model automatically estimate how effective its prompt responses would be, allowing for continued offline training without human intervention. Over time, the model is able to internally evaluate responses and select the one that will produce the greatest reward, or the one that sounds the most natural and accurate.

Beyond ensuring LLMs produce accurate and helpful responses, RLHF can be applied to improve other forms of generative AI, including image and music generation. It has also been used to train AI agents in robotics and video games. By 2019, AI systems trained with RLHF, such as DeepMind’s AlphaStar and OpenAI Five, had beat some of the world’s top professional StarCraft and Dota 2 players.

OpenAI is a pioneer in RLHF. Its proximal policy optimization (PPO) algorithm significantly reduced the cost of gathering and refining human feedback, allowing for the integration of RLHF in natural language processing. OpenAI also produced the first code explaining RLHF for language models in 2019 and, in 2022, released the RLHF-trained InstructGPT, a key program in the development of ChatGPT.

Despite its role in training AI agents, RLHF does have its limitations. Because human input is subjective, it’s difficult to determine what represents “high-quality” output. Humans can also be biased or intentionally malicious.

FAQs

What is RLHF and why is it used for large language models?

RLHF stands for reinforcement learning from human feedback and is used to make AI responses more aligned with human expectations. It helps models handle complex, non-mathematical tasks like tone, context, and humor more effectively.

How does the RLHF training process work in simple terms?

Humans review and compare an AI’s responses to prompts and score them based on quality and accuracy. The model then uses this feedback to gradually refine its behavior through trial and error.

What are the main stages of RLHF?

The process typically includes data collection, supervised fine-tuning, and training a reward model to guide future responses. This setup allows the AI to continue improving even without constant human input.

Where is RLHF used besides chatbots and text models?

RLHF is also applied in robotics, video games, and other forms of generative AI such as image and music creation. It has helped train systems like AlphaStar and OpenAI Five to compete with top human players.

What are the limitations of RLHF?

Because humans provide the feedback, the process can be influenced by subjectivity and bias. This makes it challenging to define a single standard for what counts as “high-quality” output in every situation.

About Vivek Shah

Vivek Shah is a Los Angeles entrepreneur and technology founder with experience launching consumer apps, AI platforms, and subscription-based marketplaces. He currently leads Gauge AI and directs investments across multiple sectors while remaining active in community outreach through Los Angeles Hope for Kids. His background combines hands-on startup leadership with an interest in practical AI applications and emerging training methods.