Fine-Tuning LLMs with Reinforcement Learning

Large Language Models are powerful—but not always aligned with human intent. In this session, we explore Reinforcement Learning from AI Feedback (RLAIF), a scalable alternative to RLHF that uses AI-based evaluators to train safer, more helpful models. We’ll compare RLAIF with RLHF and Direct Policy Optimization (DPO), outlining their trade-offs and practical applications. Through a hands-on walkthrough, you'll learn how to implement RLAIF using public datasets to reduce toxicity in model outputs—pushing the frontier of ethical, aligned AI development.

Key Takeaways:
Understand the limitations of prompt engineering and SFT in aligning LLMs with human values.
Explore Reinforcement Learning from AI Feedback (RLAIF) as a scalable alternative to human-guided alignment.
Learn how Constitutional AI and LLM-based evaluators can reduce toxicity and improve model behavior.
Get hands-on insights into implementing RLAIF using public datasets and evaluation pipelines.