What is RLHF (Reinforcement Learning from Human Feedback)?

What is RLHF (Reinforcement Learning from Human Feedback)?

Reinforcement Learning from Human Feedback, commonly known as RLHF, is a training methodology used to improve the behaviour of AI models by incorporating direct feedback from human evaluators. Rather than relying solely on mathematical loss functions or pre-labelled datasets, RLHF allows AI systems to learn what humans actually prefer in terms of output quality, safety, and relevance.

The technique gained widespread attention as a key ingredient behind the impressive capabilities of modern large language models. It is the process that helps transform a raw language model, which can generate text but may produce harmful, biased, or unhelpful content, into a refined assistant that responds thoughtfully and appropriately.

How RLHF Works

The RLHF process generally involves three stages:

Stage 1: Supervised Fine-Tuning

The base AI model is first fine-tuned on a curated dataset of high-quality examples. Human annotators create or select ideal responses to a variety of prompts, and the model learns to mimic these examples. This gives the model a baseline understanding of what good output looks like.

Stage 2: Reward Model Training

Next, the model generates multiple responses to the same prompt, and human evaluators rank these responses from best to worst. These rankings are used to train a separate reward model, which learns to predict how a human would rate any given output. The reward model essentially encodes human preferences into a mathematical function.

Stage 3: Reinforcement Learning Optimisation

Finally, the original AI model is optimised using reinforcement learning, specifically a technique called Proximal Policy Optimisation (PPO). The model generates outputs, the reward model scores them, and the AI model adjusts its behaviour to produce responses that earn higher scores. Over many iterations, the model becomes progressively better at generating responses that align with human preferences.

Why RLHF Matters for AI Safety

RLHF addresses a fundamental challenge in AI development: specifying exactly what you want an AI system to do. It is extremely difficult to write explicit rules that cover every situation an AI might encounter. RLHF sidesteps this problem by allowing the model to learn from examples of human judgement rather than rigid rules.

This has several practical safety benefits:

• Reducing harmful outputs: RLHF-trained models are significantly less likely to generate toxic, offensive, or dangerous content because human evaluators consistently penalise such outputs during training.
• Improving helpfulness: The technique helps models provide more useful, relevant, and accurate responses rather than technically correct but unhelpful ones.
• Handling ambiguity: When a request could be interpreted in multiple ways, RLHF-trained models tend to choose the interpretation that is most helpful and least risky.

Limitations and Challenges

Despite its effectiveness, RLHF is not a perfect solution:

Human Evaluator Quality

The quality of RLHF depends entirely on the quality of human feedback. If evaluators are biased, inconsistent, or lack domain expertise, the model will learn flawed preferences. Organisations that train AI models invest heavily in evaluator selection, training, and quality assurance processes.

Reward Hacking

AI models can sometimes find ways to earn high reward scores without actually producing better outputs. This is known as reward hacking or reward gaming. For example, a model might learn that longer responses tend to receive higher ratings and begin producing unnecessarily verbose output. Careful reward model design and ongoing monitoring are needed to mitigate this risk.

Scalability Concerns

RLHF requires significant human labour. Each round of evaluation involves human annotators reviewing multiple model outputs, which is time-consuming and expensive. As models become more capable, the expertise required of evaluators also increases, further raising costs.

Cultural and Regional Bias

Human preferences vary across cultures, languages, and regions. An RLHF process conducted primarily with evaluators from one cultural background may produce a model that performs well for that audience but poorly for others. For organisations operating across Southeast Asia's diverse markets, this is a particularly relevant consideration.

RLHF in the Southeast Asian Context

For businesses in Southeast Asia deploying or procuring AI systems, understanding RLHF has practical implications:

• Vendor evaluation: When assessing AI vendors, ask about their training methodology. Models trained with RLHF from diverse evaluator pools are more likely to perform well across different cultural contexts and languages.
• Customisation opportunities: Some organisations are beginning to apply RLHF techniques to fine-tune models for their specific use cases, using feedback from their own domain experts to improve model performance for their particular business context.
• Regulatory alignment: As ASEAN nations develop AI regulations, the ability to demonstrate that AI systems have been trained with human oversight and feedback mechanisms may become a compliance requirement.

Practical Considerations for Business Leaders

If your organisation uses or plans to use large language models, RLHF is relevant to your decision-making in several ways:

1. Model selection: Prefer models that have undergone RLHF training, as they tend to be safer and more useful out of the box.
2. Feedback loops: Consider implementing your own feedback mechanisms where employees or customers rate AI outputs, creating data that can inform future model improvements.
3. Safety expectations: Understand that RLHF significantly improves but does not eliminate the possibility of problematic outputs. Additional safety layers such as content filtering and human review remain important.
4. Cost awareness: If considering custom RLHF training, budget for the significant human annotation costs involved.