How to Use RLHF for Better AI Model Alignment with User Goals

How to Use RLHF for Better AI Model Alignment with User Goals

What is Reinforcement Learning with Human Feedback (RLHF)?

Reinforcement Learning from Human Feedback (RLHF) is an advanced machine learning technique that combines reinforcement learning with human input to improve AI model performance, particularly for complex tasks like natural language processing. With this technique, you can train AI models to generate higher-quality text outputs.

In this blog, we'll explain the RLHF training technique and its potential benefits by showing how to align language models with user intent on a wide range of tasks by fine-tuning them with human feedback.

How does RLHF work?

Reinforcement Learning (RL) trains software models to make decisions that maximize rewards, making them more accurate. RLHF trains a reward model from direct human feedback to align the model with human intents.

In simple terms, RLHF follows this process:

  1. Human Feedback: Create a set of human-generated prompts and evaluations of LLM outputs.

  2. Reward Model: Use human feedback to train a separate " reward model" using supervised learning that can emulate human preferences.

  3. Policy Optimization: Optimize the initial model using reinforcement learning to optimize LLM outputs that receive favorably.

With RLHF, the model learns directly from receiving feedback from humans. Compared to passively learning from vast amounts of data, the model is refined based on specific input.

Why is RLHF Important?

The benefits of Reinforcement Learning from Human Feedback (RLHF) for training and improving AI models are numerous. It leads to improved performance, alignment with human values, reduced bias, enhanced safety, adaptability, continuous improvement, efficient training, increased user satisfaction, leveraging domain expertise, and improved interpretability. These advantages make RLHF a valuable technique for building better AI systems, especially in conversational AI, content generation, and customer service applications. This is possible because RLHF is based on the following fundamentals.

  • RLHF enables models to adjust their behavior based on specific, relevant feedback, similar to how humans learn best from feedback.

  • Models can steadily enhance their performance by learning from errors and adjusting based on new instructions or corrections.

  • Human feedback allows models to be fine-tuned to closely match human values and expectations, guaranteeing that their outputs are safe, meaningful, and valuable.

OpenAI’s InstructGPT training using RLHF

OpenAI used Reinforcement Learning from Human Feedback (RLHF) to develop InstructGPT, a fine-tuned version of GPT-3 designed to better align with user instructions and improve model output. The primary goal was to address issues with GPT-3, such as generating untruthful or toxic outputs, and to make the model more useful and aligned with human intent by following instructions more accurately. The researchers from OpenAI published a paper - “Training language models to follow instructions with human feedback,” to document their findings.

OpenAI collected a dataset of human-written demonstrations and comparisons of model outputs. Human labelers ranked outputs based on preference. Using this dataset, a reward model was trained to predict which outputs humans preferred. The reward model served as a function to fine-tune GPT-3 using reinforcement learning, specifically the Proximal Policy Optimization (PPO) algorithm. This process aimed to maximize alignment with human preferences.

A diagram illustrating the three steps. Source - Training Models to follow instructions with human feedback. 2022 Paper

Test results from human evaluations indicate that InstructGPT models were favored over GPT-3. They displayed a higher level of adherence to instructions and generated fewer false or toxic outputs. Notably, InstructGPT showed enhancements in truthfulness and reduced the generation of toxic content compared to GPT-3. The model exhibited superior performance across various tasks, unlocking capabilities previously difficult to elicit through prompt engineering alone.

RLHF Implementation Challenges

RLHF methodology is very effective. But, there are some challenges with RLHF. According to the research paper - “Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback,” there are challenges associated with all three components of RLHF ((human feedback, reward model, and policy optimization)

  • Challenges with human feedback in RLHF— Selecting quality annotators is challenging due to the risk of personal bias and deliberate misinformation. Human annotators are susceptible to cognitive traps, and artificial interactions during data collection may not accurately reflect real-world deployment, resulting in a disconnect between training and real-world conditions.

  • Challenges with the reward model—Humans have complex and evolving preferences that are difficult to model accurately. Most research on Reinforcement Learning from Human Feedback (RLHF) does not consider the diverse nature of human preferences and capabilities, which can lead to a misrepresentation of human goals and preferences. Additionally, RLHF models are susceptible to "reward hacking," where the model finds shortcuts to minimize loss without truly learning the problem's key aspects.

  • Challenges with RLHF policy—The reinforcement learning component of RLHF is highly susceptible to adversarial attacks, even with black box models like ChatGPT and GPT-4. This vulnerability can lead to poor performance in real-world scenarios. Additionally, biases in the training dataset can unintentionally influence the RLHF process.

Key steps to consider when using RLHF

  1. Data Collection: Gather human-written prompts and desired model responses; collect human feedback on model outputs.

  2. Model Training: Start with a pre-trained language model, fine-tune it using supervised learning, and train a separate reward model to predict human preferences.

  3. Policy Optimization: Use reinforcement learning to further optimize the language model with the reward model providing the signal for the RL process.

  4. Ethical Considerations and Deployment: Address potential biases and safety concerns and ensure diverse perspectives in the feedback collection process; implement the optimized model and continuously monitor its performance.