References: Reasoning models and the road ahead

Source material

Source curriculum (structural mirror, cited as further study):
• Hugging Face, "LLM Course", Chapter 12: "Open R1 for Students"
  Authors: the Hugging Face team (Lewis Tunstall, Leandro von Werra,
    Lysandre Debut, Sylvain Gugger, Merve Noyan, and others)
  Course page: https://huggingface.co/learn/llm-course/chapter12
  Code and notebooks: https://github.com/huggingface/course
  License: Apache 2.0 (prose and code)
  Required attribution: "Based on the Hugging Face LLM Course
    (huggingface.co/learn/llm-course), © Hugging Face, used under the
    Apache 2.0 license. This is an independent structural mirror;
    Hugging Face does not endorse it."
This lesson mirrors the structure of the course's Open R1 chapter
(reinforcement learning's role in LLMs, reasoning models, and GRPO in TRL)
and serves as the track capstone. Clawdemy's lessons are original prose that
follows the pedagogical arc of the course. We do not reproduce or transcribe
the course; we cite it as the recommended companion. Course materials are
used under the Apache 2.0 license with the attribution above, which requires
a link to the license and an indication of changes, and does not permit
implying endorsement.

Read this next

• Hugging Face LLM Course, Open R1 chapter. The chapter this lesson mirrors. It breaks down reinforcement learning for LLMs, the DeepSeek R1 paper, and a practical GRPO-in-TRL walk-through, the hands-on continuation once this capstone’s map makes sense.

Going deeper

A short, durable list. Each link is a specific next step, not a generic pile.

• Open R1 (GitHub). The open community project to reproduce reasoning models. Reading its README and issues shows how a frontier capability gets rebuilt in the open, and how to contribute.

• The TRL library documentation. Home of both SFTTrainer (lesson 10) and the GRPO trainer for reasoning. The reference for the full pretrain-to-SFT-to-RL training stack in one library.

• The Hugging Face blog. How to keep up after the track. New capabilities, models, and techniques are written up here as they land, which is the practical answer to “the frontier keeps moving.”

Adjacent topics

Where this connects inside the track.

• Fine-tuning LLMs (lesson 10). Reasoning training (RL) is the next stage after the SFT you learned there, and it uses the same TRL library. Pretrain, SFT, then RL.

• What transformers do (lesson 1). The track’s bookend: lesson 1’s working picture (tokens in, tokens out, attention in the middle) still describes the reasoning models at today’s frontier.

• The main NLP tasks (lesson 7). The model-agnostic applied loop introduced there is exactly what makes this track’s method survive the moving frontier this lesson describes.