sakshisukla
Member
Reinforcement Learning from Human Feedback (RLHF) is a technique that aligns complex machine learning models—particularly large language models—with human preferences and values by incorporating evaluative signals directly from people. Rather than relying solely on predefined reward functions (which can be difficult to specify for nuanced tasks), RLHF leverages human judgments to guide model behavior toward more desirable outputs.
The RLHF process typically unfolds in three stages:
Key advantages of RLHF include its ability to capture subtleties of human preference (such as tone, style, or factual accuracy) and to mitigate undesirable behaviors (like toxicity or incoherence) by directly penalizing them in the reward signal. However, challenges remain: collecting high-quality human feedback can be costly and time-consuming, and poorly designed feedback protocols may introduce bias.
By integrating human evaluative insights into the training loop, RLHF produces more robust, trustworthy, and user-aligned generative models. Consider deepening your understanding and practical mastery of these cutting-edge alignment methods in our Generative AI online course.
The RLHF process typically unfolds in three stages:
- Supervised Fine-Tuning (SFT):
A base model is first fine-tuned on a curated dataset of input–output pairs that humans have labeled as high-quality. This stage grounds the model in human-like responses and provides a solid initialization for subsequent reward learning. - Reward Model Training:
Humans compare pairs or sets of model-generated outputs in response to the same prompt, ranking them by preference. These comparisons are used to train a separate “reward model” that predicts human preference scores. The reward model effectively learns what humans find useful, harmless, or engaging without requiring explicit numerical reward definitions. - Policy Optimization via RL:
The fine-tuned base model (the “policy”) is further optimized using reinforcement learning, typically through Proximal Policy Optimization (PPO). The policy generates candidate outputs, the reward model scores them, and PPO adjusts the policy’s parameters to maximize expected reward. Over many iterations, the policy learns to produce outputs that align more closely with human judgments.
Key advantages of RLHF include its ability to capture subtleties of human preference (such as tone, style, or factual accuracy) and to mitigate undesirable behaviors (like toxicity or incoherence) by directly penalizing them in the reward signal. However, challenges remain: collecting high-quality human feedback can be costly and time-consuming, and poorly designed feedback protocols may introduce bias.
By integrating human evaluative insights into the training loop, RLHF produces more robust, trustworthy, and user-aligned generative models. Consider deepening your understanding and practical mastery of these cutting-edge alignment methods in our Generative AI online course.