About this event
Reinforcement Learning from Human Feedback (RLHF) is the final ingredient that makes frontier language models so powerful. Unfortunately, due to its technical complexity, the technique has remained the preserve of large model builders. Now, new tooling is making the technology more accessible, allowing businesses to train smaller open models that outperform traditional fine-tuning methods and closed models for specific business tasks.
This webinar comprehensively explores RLHF, covering its fundamental principles, significance in AI development, and how it can be practically implemented by all businesses. We'll also dive deep into the operational challenges of creating RLHF datasets, including scale issues, human expertise, and quality control.
Key takeaways:
- The core principles behind RLHF and how it works
- The benefits of applying RLHF to your business use cases
- Real challenges and solutions in creating RLHF datasets
- Practical strategies for implementing RLHF in your projects
Whether you're looking to implement RLHF for the first time or optimize your existing workflows, this webinar is designed to provide valuable, actionable knowledge.
Hosted by
-
Guest speaker
Daniel Hesslow Cofounder & Research Scientist @ Adaptive MLDH G
Daniel is a ML research scientist and co-founder of Adaptive ML, a pioneer in the field of LLM training platforms. Prior to founding Adaptive ML, Daniel was a technical lead behind the popular Falcon 40B and 180B open-source LLMs, and helped build the RefinedWeb dataset used to train them. Daniel was also a key contributor to the development of the open-source LLM BLOOM and part of the Extreme Scale team at Hugging Face. As a seasoned expert in frontier LLM development, Daniel brings a unique perspective on the use of open LLMs and how reinforcement learning can unlock their potential against closed LLM alternatives.
-
Guest speaker
Andrew Jardine Head of GTM @ Adaptive MLAJ G
Andrew is currently the Head of GTM at Adaptive ML, where he drives customer engagement and adoption of reinforcement learning. Before joining Adaptive ML, Andrew championed open-source ML as the GTM Exec at Hugging Face and has worked in the ML industry since 2016. Outside of his role at Adaptive ML, Andrew leads the Toronto chapter of the MLOps community and actively shares ML research content on social platforms, making complex ML topics accessible to a broader audience.
-
Team member
Paul GraffanPG T
Kili Technology
Unique Data for Frontier AIKili Technology delivers large-scale custom, unique, and high-quality data for evaluating and fine-tuning large language models. We provide carefully tailored high-quality data for large language models delivered efficiently and at scale.
