Large Language Models (LLMs) and Reinforcement Learning (RL)

• Basic understanding of Machine Learning

Audience

• Data scientists
• Software engineers

Large Language Models (LLMs) are advanced types of neural networks designed to understand and generate human-like text based on the input they receive. Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize cumulative rewards.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data scientists who wish to gain a comprehensive understanding and practical skills in both Large Language Models (LLMs) and Reinforcement Learning (RL).

By the end of this training, participants will be able to:

• Understand the components and functionality of transformer models.
• Optimize and fine-tune LLMs for specific tasks and applications.
• Understand the core principles and methodologies of reinforcement learning.
• Learn how reinforcement learning techniques can enhance the performance of LLMs.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Large Language Models (LLMs)

• Overview of LLMs
• Definition and significance
• Applications in AI today

Transformer Architecture

• What is a transformer and how does it work?
• Main components and features
• Embedding and positional encoding
• Multi-head attention
• Feed-forward neural network
• Normalization and residual connections

Transformer Models

• Self-attention mechanism
• Encoder-decoder architecture
• Positional embeddings
• BERT (Bidirectional Encoder Representations from Transformers)
• GPT (Generative Pretrained Transformer)

Performance Optimization and Pitfalls

• Context length
• Mamba and state-space models
• Flash attention
• Sparse transformers
• Vision transformers
• Importance of quantization

Improving Transformers

• Retrieval augmented text generation
• Mixture of models
• Tree of thoughts

Fine-Tuning

• Theory of low-rank adaptation
• Fine-Tuning with QLora

Scaling Laws and Optimization in LLMs

• Importance of scaling laws for LLMs
• Data and model size scaling
• Computational scaling
• Parameter efficiency scaling

Optimization

• Relationship between model size, data size, compute budget, and inference requirements
• Optimizing performance and efficiency of LLMs
• Best practices and tools for training and fine-tuning LLMs

Training and Fine-Tuning LLMs

• Steps and challenges of training LLMs from scratch
• Data acquisition and maintenance
• Large-scale data, CPU, and memory requirements
• Optimization challenges
• Landscape of open-source LLMs

Fundamentals of Reinforcement Learning (RL)

• Introduction to Reinforcement Learning
• Learning through positive reinforcement
• Definition and core concepts
• Markov Decision Process (MDP)
• Dynamic programming
• Monte Carlo methods
• Temporal Difference Learning

Deep Reinforcement Learning

• Deep Q-Networks (DQN)
• Proximal Policy Optimization (PPO)
• Elements of Reinforcement Learning

Integration of LLMs and Reinforcement Learning

• Combining LLMs with Reinforcement Learning
• How RL is used in LLMs
• Reinforcement Learning with Human Feedback (RLHF)
• Alternatives to RLHF

Case Studies and Applications

• Real-world applications
• Success stories and challenges

Advanced Topics

• Advanced techniques
• Advanced optimization methods
• Cutting-edge research and developments

Summary and Next Steps