Practical Tips and Solutions for Implementing RLHF

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.


Get to know our speakers

Group 1473

Andrew Jardine
Head of GTM
Adaptive ML

Paul G

Paul Graffan
AI Alignment and Safety Expert
Kili Technology

Group 1476

Daniel Hesslow
Cofounder & Research Scientist
Adaptive ML

Andrew Jardine

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.

Paul Graffan

Paul Graffan leads AI Safety and Alignment initiatives at Kili Technology. With extensive experience in collaborating with global enterprises, he has developed cutting-edge data strategies to enhance NLP and CV model performance. Currently, his focus involves partnering with prominent LLM developers, including Mistral, to tackle the challenges of generating high-quality alignment data and human feedback. Paul's expertise bridges theoretical RLHF concepts and practical implementation in enterprise environments, making him uniquely qualified to address the complex operational aspects of RLHF deployment.

Daniel Hesslow

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.


Kili Technology x Adaptive ML

Kili Technology

Today, LLM developers and fine-tuners are focused on unlocking the full potential of their frontier AI models. However, much of this potential remains untapped due to a shortage of high-quality data for training and fine-tuning LLMs. Additionally, there is a lack of quality human feedback to evaluate, align, and ensure the safety of these models. At Kili Technology, we address this challenge by providing custom, large-scale, high-quality datasets and human feedback. This helps machine learning engineers achieve their goal of delivering truly valuable models.

Adaptive ML

Adaptive ML provides an LLM platform, Adaptive Engine, enabling companies to privately tune, deploy, and evaluate open-source models by tuning on user interactions. Founded by the engineers behind the popular Falcon 40B and 180B open models, Adaptive ML's mission is to democratize access to preference tuning techniques that allow businesses to achieve frontier performance with smaller, private, task-specific models. Adaptive Engine supports this by democratizing state-of-the-art alignment techniques, including RLHF, to learn directly from user and employee interactions—while also simplifying model evaluation and deployment.

Unique Data for Frontier AI

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LLM Alignment
Kili Technology delivers large-scale RLHF and SFT high-quality data through a comprehensive managed service, domain and language expertise, and a meticulous focus on quality. 
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LLM Evaluation
We employ state-of-the-art frameworks to deliver consistent, unbiased evaluations and in a short time-frame to match your model delivery cycles.