Reinforcement learning from human feedback (RLHF) is a technique in machine learning where AI models learn behaviours through direct human feedback instead of more traditional reward functions, effectively improving their performance while better aligning the AI with human goals and expectations.
Most modern AI language models are surprisingly adept at generating text that is accurate, relevant and human-like. Unfortunately, even with all these capabilities, they do not always create content that a user might consider 'good'. This is, at least in part, because 'good' is such a difficult concept to define—different individuals want different things from AI language models, and what makes a good response will naturally vary with the user's standards and the context of the situation.
Traditional AI training methods do little to address these concerns. Instead, they are typically designed to predict the most likely next word in a sequence based on the actual sequences of words presented in their data sets. Metrics may be employed to compare generated content to specific reference texts, but they still leave something to be desired. In the end, only human judgement can determine whether AI generated text is 'good'. This is the reasoning behind reinforcement learning from human feedback, or RLHF.
RLHF is a method used to refine AI language models beyond traditional training approaches. It involves training the model based on preferences or corrections provided by living humans. Rather than merely predicting the word sequences by reviewing data, AI can apply RLHF to align more closely with human ideas of what constitutes a good or useful response according to human standards. RLHF as a concept was first suggested by OpenAI in 2019 and is an evolution of reinforcement learning (RL).
Reinforcement learning from human feedback and traditional reinforcement learning are both machine learning (ML) methods for training AI systems, but they differ significantly in how they guide the learning process. Traditional RL relies on reward signals from the environment, which means the AI receives feedback from its actions within a predefined set of automations, learning to maximise these rewards through trial and error. This automated feedback helps define what is accurate or natural but does not necessarily align with complex human preferences.
In contrast, RLHF incorporates direct human feedback into the learning loop, providing the AI with real, contextually relevant insights into what humans consider high-quality or desirable outcomes. This method allows the AI to learn not just to perform tasks but to adapt its responses according to human judgements, making it more effective for applications where human-like understanding is essential.
RLHF is a unique approach to training AI language models—one that involves several critical steps designed to bring the AI more closely in line with human expectations and values. The key aspects of these steps include:
The foundation of RLHF involves pretraining a language model on a large corpus of text data. This phase allows the model to learn a wide range of language patterns and contexts before any of the more specialised training occurs.
Pretraining equips the AI with general linguistic abilities, enabling it to understand and generate coherent text. This step typically uses unsupervised learning techniques, where the model learns to predict the next word in sentences without any explicit feedback on the quality of its outputs.
Once the initial pretraining is complete, the next step involves gathering data specifically designed for training a reward model. This model is fundamental to RLHF, as it translates human evaluations of the model's text outputs into a numerical reward signal.
Training an RLHF reward model starts by collecting human feedback on the outputs generated by the LM. This feedback could include direct rankings, ratings or choices between available options. The gathered data is then used to teach the reward model to estimate how well the text aligns with human preferences. The effectiveness of the reward model hinges on the quality and volume of human feedback.
The final stage of the RLHF process involves fine-tuning the pretrained language model using the trained reward model through reinforcement learning techniques. This stage adjusts the LM's parameters to maximise the rewards it receives from the reward model, effectively optimising the text generation to produce outputs that are more aligned with human preferences.
The use of reinforcement learning allows the model to iteratively improve based on continuous feedback, enhancing its ability to generate text that meets specific human standards or achieves other specified goals.
Reinforcement learning from human feedback represents a significant advancement in AI training, moving beyond traditional methods to incorporate direct human insights into model development. Simply put, it can do more than just predict what words should (statistically speaking) come next in a sequence. This brings the world closer to creating AI language models that can provide truly intelligent responses.
Of course, there are many more-immediate advantages to RLHF, particularly where businesses are concerned. This approach to AI training allows for several noteworthy benefits, such as:
Reducing training time
By integrating direct feedback, RLHF speeds up the learning process, allowing models to achieve desired results more quickly. This can be applied to internal and external chatbots, allowing them to understand and respond to diverse user inquiries more quickly.Allowing for more complex training parameters
RLHF can handle subtle and sophisticated training scenarios that traditional models may not, using human judgement to guide learning and establish parameters in areas that would otherwise be considered subjective. Content recommendation systems can benefit from this aspect of RLHF, adjusting to subtle variations in user preferences over time.Improving AI performance
Models trained with RLHF typically exhibit better performance, as they are continually refined through iterative feedback to better meet human standards. Enhancing the performance of language translation tools with RLHF produces more natural and contextually relevant translations.Mitigating risk
Incorporating human feedback ensures that AI systems act in ways that are expected and intended, minimising the risk of harmful or unintended behaviours. For example, the deployment of autonomous vehicles benefits from more human oversight in AI training.Enhancing safety
Training models with a focus on human feedback ensures that AI systems act in ways that are safe and predictable in real-world scenarios. Improving medical diagnostic systems with RLHF helps AI-enhanced health providers avoid harmful recommendations and better prioritise patient safety.Helps uphold ethics
RLHF allows models to reflect ethical considerations and social norms, ensuring AI decisions are made with human values in mind. Biases can be more immediately identified and eliminated, preventing it from seeping into generated social posts or other branded content.Increasing user satisfaction
By aligning AI outputs more closely with human expectations, RLHF improves the overall user experience.Ensuring continuous learning and adaptation
RLHF models adapt over time to new information and changing human preferences, maintaining their relevance and effectiveness.
While reinforcement learning from human feedback offers numerous benefits, it also carries with it several challenges that can impede its effectiveness in business. Understanding these following challenges is crucial for organisations considering RLHF as an option for enhancing their AI systems:
The need for continuous human input can make RLHF a costly prospect, particularly because expert annotators are needed to provide accurate and useful feedback. Automating parts of the feedback process through machine learning techniques can provide a partial solution, reducing some of the dependence on human input, thus lowering costs.
Human judgements can vary widely and are often influenced by individual biases. This can affect the consistency and reliability of the training data. To counter this risk, use a diverse group of human annotators capable of providing a more balanced perspective on the AI's performance.
Living annotators won't always agree on what constitutes a 'good' or 'useful' response, which can lead to inconsistent or contradictory evaluations. To ensure solidarity, conflict resolution mechanisms and consensus-building strategies may be employed among review teams to encourage more harmonised feedback.
Incorporating human feedback into AI training may seem like a less complicated approach when compared to more autonomous training methods. The reality is that RLHF nonetheless leverages complex mathematical models to optimise AI behaviour based on nuanced human input. This sophisticated approach blends human evaluative feedback with algorithmic training to guide AI systems, making them more effective and responsive to human preferences.
The following are essential components involved in this process:
The state space in RLHF represents all the relevant information available to the AI at any given point during its decision-making process. This includes all variables that could influence its decisions, whether they are already provided or need to be inferred. The state space is dynamic, changing as the AI interacts with its environment and gathers new data.
The action space is extraordinarily vast, encompassing the complete set of responses or text generations that the AI model could possibly produce in response to a prompt. The enormity of the action space in language models makes RLHF particularly challenging but also incredibly powerful for generating contextually appropriate responses.
The reward function in RLHF quantifies the success of the AI's actions based on human feedback. Unlike traditional reinforcement learning, where rewards are predefined and often simplistic, RLHF uses human feedback to create a more nuanced reward signal. The feedback assesses the AI's outputs based on quality, relevance or adherence to human values, converting this assessment into a quantitative measure that drives learning.
Constraints are used to guide the AI away from undesirable behaviours. These could be ethical guidelines, safety considerations, or simply established limits within which the AI must operate. For example, a language model might be penalised for generating offensive content or deviating too far from a topic. Constraints help ensure that the AI's outputs remain within the bounds of what is considered acceptable or intended by the human trainers.
The RLHF policy dictates the AI's decision-making process, mapping from the current state to the next action. This is essentially the model's behaviour guideline, which is optimised continuously based on the reward feedback. The policy's goal is to maximise the cumulative reward, thereby aligning the AI's actions more closely with human expectations and preferences.
As a powerful and innovative approach to AI language training, RLHF is also having a clear impact on the related field of generative AI (GenAI). This makes possible more insightful, contextually appropriate outputs across various generative applications. Examples of how RLHF can be applied to GenAI include:
RLHF extends its utility beyond language models to other forms of generative AI, such as image and music generation. For example, in AI image generation, RLHF can be used to evaluate and enhance the realism or emotional impact of artworks, crucial for applications in digital art or advertising. Similarly, RLHF in music generation helps create tracks that resonate better with specific emotional tones or activities, increasing user engagement in areas like fitness apps or mental health therapy. This can take GenAI beyond the more common application of generating written content.
In voice technology, RLHF refines the way voice assistants interact with users, making them sound more friendly, curious, trustworthy etc. By training voice assistants to respond in increasingly human-like ways, RLHF increases the likelihood of user satisfaction and long-term engagement.
Considering that what is considered 'helpful' or 'appealing' can vary greatly between individuals, RLHF allows customisation of AI behaviours to better meet diverse user expectations and cultural norms. Each model can be trained with feedback from different groups of people, which allows for a wider range of human-like responses that are more likely to satisfy specific user preferences.
RLHF is a human-centric approach to AI training, making it undeniably advantageous for language models designed to interact directly with users. ServiceNow, the leader in workflow automation, has harnessed this concept.
ServiceNow's award-winning Now Platform® is fully integrated with advanced AI capabilities capable of supporting your business' RLHF strategies. With features designed to enhance user experiences and streamline operations, the Now Platform facilitates the creation and maintenance of intelligent workflows that can adapt based on user feedback and interactions.
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