Reinforcement Learning, Kernels, Reasoning, Quantization & Agents

Abstract

Why is Reinforcement Learning (RL) suddenly everywhere, and is it truly effective? Have LLMs hit a plateau in terms of intelligence and capabilities, or is RL the breakthrough they need?

In this workshop, we’ll dive into the fundamentals of RL, what makes a good reward function, and how RL can help create agents.

We’ll also talk about kernels, are they still worth your time and what you should focus on. And finally, we’ll explore how LLMs like DeepSeek-R1 can be quantized down to 1.58-bits and still perform well, along with techniques to maintain accuracy.

About Daniel Han

I’m building Unsloth and we’re an open-source startup trying to make AI more accessible and accurate for everyone! We have 40K GitHub stars, 10M monthly downloads on Hugging Face and worked with Google, Meta, Hugging Face teams to fix bugs in open-source models like Llama, Phi & Gemma models. I was previously working at NVIDIA making TSNE 2000x faster.