Reinforcement Learning from Human Feedback (RLHF)

RLHF is not a single step but a structured, sequential process. It begins with Supervised Fine-Tuning (SFT) on high-quality demonstration data to establish a baseline. Next, a Reward Model (RM) is trained to predict human preferences by ranking multiple model outputs. Finally, the main model is optimized via Proximal Policy Optimization (PPO) against the reward model's scores, refining its policy to maximize human-preferred outputs.

The core of RLHF is learning a reward function from human judgments. Annotators rank multiple model outputs for the same prompt. A separate neural network, the reward model, is then trained via a ranking loss (e.g., Bradley-Terry) to predict which output humans would prefer. This model converts qualitative human preferences into a quantitative, differentiable signal that can guide reinforcement learning.

In the final stage, the language model's policy (its probability distribution over tokens) is fine-tuned using the Proximal Policy Optimization (PPO) algorithm. PPO updates the model to generate text that receives high scores from the reward model while using a KL-divergence penalty to prevent the policy from deviating too far from its original, linguistically coherent state. This balances reward maximization with output stability.

RLHF creates a scalable bridge between human values and machine learning. Instead of requiring humans to manually craft a perfect reward function, RLHF learns the reward function from data. This allows the system to capture nuanced, implicit human preferences about tone, safety, and style that are difficult to codify in rules. The process is iterative; as models improve, new rounds of human feedback can be collected to further refine alignment.

RLHF is distinguished by its direct reliance on human-labeled preference data. In contrast:

• Constitutional AI (CAI) uses a set of written principles (a constitution) and a self-critique loop where the model critiques and revises its own outputs.
• Reinforcement Learning from AI Feedback (RLAIF) replaces human labelers with an AI (often guided by a constitution) to generate the preference data, aiming for greater scalability. RLHF provides the foundational preference-learning mechanism that both CAI and RLAIF can build upon.

RLHF is the standard technique for aligning large language models to be helpful assistants and for training chat models. Its key limitation is the cost and complexity of collecting high-quality human preference data at scale. It can also lead to reward hacking, where the model optimizes for superficial reward signals without genuine understanding. Newer algorithms like Direct Preference Optimization (DPO) offer a simpler, more stable alternative by bypassing the explicit reward modeling step.

A framework for governing AI behavior by training models to adhere to a predefined set of core principles or a 'constitution'. It often uses self-critique loops and AI-generated feedback to align outputs with ethical and safety constraints, reducing reliance on continuous human oversight.

• Core Mechanism: Models critique and revise their own outputs against constitutional principles.
• Scalability: Enables alignment using AI feedback (RLAIF) as a complement to human feedback (RLHF).

An alignment technique where a model's behavior is fine-tuned using preferences generated by another AI system, often based on a set of constitutional principles. It serves as a scalable alternative to human feedback (RLHF) for initial alignment and iterative improvement.

• Process: An AI preference model judges outputs, training a reward model to guide policy optimization.
• Use Case: Rapid generation of large-scale preference data for cost-effective alignment.

A stable and efficient algorithm for aligning language models with human preferences. DPO bypasses the need to train a separate reward model by directly optimizing the policy using a dataset of preferred and dispreferred responses.

• Advantage: More stable than traditional RLHF, avoiding reward hacking and complex reinforcement learning loops.
• Mechanism: Derives a closed-form solution linking the reward function to the optimal policy under a Bradley-Terry preference model.

An architectural component, central to Constitutional AI, where a language model evaluates its own proposed outputs against a set of principles. It identifies potential violations and revises its response before final generation.

• Function: Provides an internal alignment mechanism, enabling the model to adhere to constraints without external filtering.
• Implementation: Often structured as a chain-of-thought process where the model asks, "Does this response violate principle X?"

The machine learning task of training a model to predict human or AI preferences between different outputs. In RLHF/RLAIF, this is typically a reward model trained on pairwise comparisons to capture nuanced judgments about quality, safety, and alignment.

• Training Data: Requires datasets of human or AI-labeled preferences (e.g., Output A is better than Output B).
• Output: A scalar reward signal used to fine-tune the main policy model via reinforcement learning.

The process of using specialized machine learning models to automatically detect and categorize potentially harmful, toxic, or unsafe content. A safety classifier is a model fine-tuned to analyze text for specific risk categories.

• Categories: Toxicity, violence, unethical advice, privacy violations, and misinformation.
• Deployment: Used as a filter for model inputs/outputs or as a reward signal during RLHF training to discourage harmful generations.