Optimizing LLMs with RLHF

Reinforcement Learning with Human Feedback (RLHF) is a well-known technique in machine learning. It is essential for developing LLMs that are aligned with human values and expectations, provide high-quality and reliable outputs, enhance user satisfaction and trust, adapt to diverse tasks, and ensure ethical and safe AI development.

Let’s dive into more technical details on how it is being implemented in general and specifically for LLMs. Most importantly, what are the pros and cons of it in the context of LLMs?

RLHF is a machine learning technique that improves AI training by adding human feedback to the process. Here’s a simple explanation:

1. Reinforcement Learning Basics: Reinforcement Learning Basics: In standard RL, an agent learns by performing actions in an environment to earn rewards or penalties, improving its decisions over time.
2. Human Feedback Integration: In RLHF, humans provide additional feedback on the agent’s actions, such as ranking outputs, giving rewards, or correcting actions. This extra input helps the agent learn more effectively, especially when traditional rewards are unclear or insufficient.

Implementing RLHF in LLMs involves a few key steps to ensure that the model not only generates text but also aligns its outputs with human values and expectations. Here’s a simplified explanation of the process:

1. Initial Training: The LLM is initially trained on a large text corpus using supervised learning to learn language patterns and generate coherent text.
2. Collecting Human Feedback: Human evaluators review and rate the model’s responses based on relevance, coherence, and appropriateness.
3. Reward Modeling: A reward model, a separate neural network, is trained to predict human feedback, helping the LLM understand preferred responses.
4. Policy Optimization: The LLM is fine-tuned with reinforcement learning, using the reward model’s evaluations to adjust its generation policy. Techniques like Proximal Policy Optimization (PPO) ensure stable and efficient updates.
5. Iterative Process: This cycle of generating responses, collecting feedback, and fine-tuning is repeated iteratively to improve the model’s alignment with human expectations continuously.

Implementing RLHF in LLM-based applications such as Retrieval-Augmented Generation (RAG), Fine-Tuning (FT), and Prompt Engineering (PE) can present several challenges and drawbacks. Below are some of the main cons:

1. Right now, only foundation model companies and big tech can afford it in its current form at scale as it is complex and costly.
2. Designing reward models for LLM-based applications is non-trivial. Poorly designed reward models can misguide the LLM, leading to suboptimal or undesirable outcomes.
3. Collection of human feedback is usually done via a very simple mechanism: they are prompted to choose from two options of generations for each given prompt. This aims to reduce the labeling costs but provides a weak learning signal for the reward model that will be trained on this data.
4. With traditional RLHF, the task-specific feedback may not generalize well to other tasks or domains. This will lead to no versatility or reliability.

Feedback Intelligence (FI, formerly Manot) offers a robust alternative to traditional RLHF by providing an integrated SaaS platform designed to streamline the feedback loop for LLM-based products. Here’s how FI helps overcome the drawbacks of RLHF:

• Effortless Feedback Consolidation

Evaluators do not have sufficient context of the task and that’s the reason they introduce bias. FI’s Connectors automatically collect explicit and implicit feedback directly from end-users who have all the context. This ensures consistent, unbiased, and scalable feedback collection from diverse user interactions.

• Actionable Insights and Efficient Optimization:

FI’s Insights analyzes feedback to derive actionable insights and identify the root causes of issues. This enables targeted improvement of LLM-based products for faster issue resolution using the end-user expectations. Additionally, it reduces computational costs and resource demands compared to traditional RLHF methods. We achieve this by integrating traditional Deep Learning models with unsupervised learning and employing LLM-as-a-judge in a novel orchestration methodology.

co-author Haig Douzdjian