RLHF, PPO, and DPO: How AI Models Learn from Human Preferences

Reinforcement Learning with Human Feedback (RLHF) has been one of the most impactful techniques for improving large language models (LLMs). It allows AI systems to align with human preferences, making them more useful, safe, and engaging. This approach played a significant role in the success of ChatGPT and other models, helping them generate responses that feel more natural and appropriate. 
However, RLHF is not without its challenges, which have led to the exploration of alternative methods like Direct Preference Optimization (DPO). While both approaches enhance AI training, they take different paths to refining a model’s behaviour. At the heart of RLHF is Proximal Policy Optimization (PPO), a reinforcement learning algorithm that ensures stable and controlled learning. 

How RLHF Works 

RLHF is a policy-based learning method where human reviewers compare AI-generated responses and rank them by preference. This ranking data trains a reward model, which estimates how well a response aligns with human expectations. The reward model then guides the policy updates of the AI, ensuring it generates better responses over time. 

A crucial part of RLHF is the Actor-Critic framework, where: 

  • The Actor (the language model) generates responses. 
  • The Critic (the reward model) evaluates those responses based on human preferences. 
  • The feedback is then used to improve the model’s response-generation policy. 

The Role of PPO in RLHF 

Proximal Policy Optimization (PPO) is the reinforcement learning algorithm used in RLHF to update the model’s policy iteratively. Unlike traditional reinforcement learning methods, PPO prevents drastic changes in behaviour, ensuring stable learning while refining response quality. 
Here’s why PPO is widely used in RLHF: 

1. Controlled Policy Updates

PPO limits how much the model can shift its behaviour in a single training step, preventing sudden, undesirable changes. 

2. Efficient Learning

It strikes a balance between exploration (trying new responses) and exploitation (refining existing responses). 

3. Improved Stability

Many reinforcement learning algorithms suffer from instability, but PPO’s approach to gradual updates makes it more reliable for training large-scale AI models. 

Major AI models, including ChatGPT and Llama 2-Chat, have used PPO within RLHF to fine-tune their responses. Meta, for example, saw a significant improvement in helpfulness and harmlessness in Llama 2 after multiple rounds of RLHF powered by PPO. 

Challenges of RLHF and PPO 

While RLHF combined with PPO is effective, it comes with some drawbacks: 

1. High Costs

Collecting human preference data is expensive—Meta reportedly spent $10-20 million just on annotating responses for Llama 2.

2. Time-Intensive

Human reviewers must evaluate thousands of responses, making the training process slow. 

3. Limited Scalability

In areas where preference data is scarce, RLHF becomes difficult to implement effectively. 

Due to these challenges, some AI labs have started exploring alternative training methods like Direct Preference Optimization (DPO). 

DPO: A Simpler Alternative to RLHF 

Direct Preference Optimization (DPO) offers a more straightforward approach to aligning AI models. Unlike RLHF, DPO does not rely on a separate reward model. Instead, it optimizes the model’s behaviour directly using human preference data. 
Here’s how DPO works: 

  • It compares a fine-tuned model’s responses to those of a reference model (usually its previous version). 
  • It uses a binary cross-entropy loss function to encourage the model to favour responses that align with human preferences. 
  • This direct optimization allows the model to improve without the complexity of training a separate reward model. 

Advantages of DPO Over RLHF 

  • More Stable: Since it avoids reinforcement learning’s instability, DPO is less likely to cause unexpected behaviour shifts. 
  • Lower Compute Requirements: RLHF with PPO requires substantial computational resources, while DPO is more lightweight. 
  • Easier to Implement: Without the need for a reward model, DPO simplifies the training process. 

Meta used DPO instead of RLHF for Llama 3, finding it to be more effective and computationally efficient. However, DPO’s success hinges on the quality of human preference data, meaning extra attention must be given to how this data is collected and processed

RLHF vs. DPO: Which One is Better? 

Both RLHF and DPO have their strengths and weaknesses. 

  • RLHF with PPO is more powerful when a large dataset of human preferences is available, but it is expensive and difficult to scale. 
  • DPO is more computationally efficient and easier to implement but requires well-curated preference data for optimal results. 

In practice, AI labs choose between these methods based on their available resources and the specific goals of their models. Some organizations even use a hybrid approach, combining elements of RLHF and DPO to maximize benefits. 

Conclusion

The evolution of AI training methods is driven by the need for efficiency, scalability, and alignment with human values. While RLHF with PPO has been the dominant approach, DPO is emerging as a compelling alternative. The future of AI training will likely involve a mix of these techniques, balancing accuracy, cost, and computational demands. 
As AI continues to advance, understanding these training methods will be key to developing models that are not just powerful but also responsible and aligned with human expectations. VE3 is committed to helping organizations develop advanced AI solutions. We  provide tools and expertise that align innovation with impact. Together, we can create AI solutions that work reliably, ethically, and effectively in the real world. contact us or visit us for a closer look at how VE3 can drive your organization’s success. Let’s shape the future together.

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