Definition
Reinforcement learning from human feedback (RLHF) is a machine learning technique that uses direct feedback from humans to train a “reward model”, which is then used to enhance the performance of an artificial intelligence agent through reinforcement learning [1]. RLHF is mainly utilized in natural language processing (NLP) for AI agent understanding in applications such as chatbot and conversational agents, text to speech and summarization.
A good illustration of Reinforcement Learning from Human Feedback (RLHF) concept is a scenario where a healthcare chatbot always suggests only expensive private clinics to patients. But after RLHF, it uses human feedback such as user's location and budget to recommend a mixture of government hospitals, private clinics, and traditional medicine options to patients.
How RLHF works [2].
Origin
The quest for RLHF started with the broader concept of traditional reinforcement learning (RL), a machine learning technique where an agent learns to make decisions by performing actions and receiving rewards or penalties. These early RL models, dating as back as the 1950s, were relatively simplistic like early computer programs learning to play simple games through trial and error. It wasn’t until the late 2010s, that the first serious explorations of integrating human feedback directly into learning processes began [3]
Context and Usage
The applications
of RLHF have been particularly transformative in several domains such as the
following:
Recommendation
Systems: Creating more personalized and accurate recommendation engines
Natural Language
Processing: Large language models such as Claude uses RLHF to produce more
coherent, contextually appropriate, and ethically aligned responses
Robotics:
Training robots to understand and execute complex, nuanced human instructions
Why it Matters
RLHF is very important as its integration into AI platforms has the ability to reconcile human intelligence and machine self-sufficiency. By involving human feedback, RLHF encourages greater transparency and interpretability in their decision-making processes, in addition to improving the learning capabilities of AI systems. Also, RLHF is very crucial in solving problems with biased or incomplete data, as human input serves as a corrective mechanism that reduces algorithmic shortcomings [4].
In Practice
A real-life case
study of RLHD in practice can be seen in the case of Surge AI. After learning
of Surge AI’s work with other key AI labs and large language model companies,
Anthropic began leveraging the Surge AI LLM platform for their RLHF human
feedback needs.
According to Jared Kaplan, Anthropic Co-Founder, “The team at Surge AI understands the unique challenges of training large language models and AI systems. Their human data labeling platform is tailored to provide the unique, high-quality feedback needed for cutting-edge AI work. Surge AI is an excellent partner to us in supporting our technical AI alignment research." [5]
See Also
Related Learning
Approaches:
- Reinforcement Learning: Learning approach where agents learn through trial and error using rewards and penalties
- Similarity Learning: Machine learning approach that teaches models to measure similarity between objects
- Singularity: Hypothetical point when AI surpasses human intelligence across all domains
- Strong AI: Theoretical AI with human-level general intelligence across all domains
- Supervised Learning: Learning from labeled data with clear input-output mappings
References
- Bergmann, D. (2023). What is reinforcement learning from human feedback (RLHF)?
- Twine AI. (2023). What is Reinforcement Learning from Human Feedback (RLHF) and How Does it Work?
- Lowe, h. (2025). The origins of reinforcement learning with human feedback (RLHF)
- Lark Editorial Team. (2023). RLHF Reinforcement Learning From Human Feedback
- Chen, E. (2025). How Anthropic uses Surge AI to Train and Evaluate Claude