TRL Fine-Tuning Guide
Teaches the agent to fine-tune language models with SFT, DPO, PPO/GRPO and reward models using HuggingFace TRL
- Repository
- Orchestra-Research/AI-Research-SKILLs
- Stars
- 14,195
- Licence
- MIT
- Files
- 5 files
What it does
When installed, the agent gains a detailed playbook for post-training language models with the HuggingFace TRL library. It can write code for supervised fine-tuning on instruction data, preference alignment with DPO, reward model training, and online reinforcement learning with PPO or GRPO, following step-by-step workflows and dataset formats. It is a guide for writing and running training code, not a trained model itself.
Great for
- ML engineers who want an agent to draft TRL training scripts quickly
- Developers learning the difference between SFT, DPO, and RLHF pipelines
- Researchers prototyping preference alignment on small open models
Skip it if
- People without GPU access or ML experience, since running these trainings needs substantial compute and technical setup
- Production alignment work without evaluation, because the skill's evaluation step is a single example generation and does not check safety or quality rigorously
- Users expecting it to fine-tune a model on their behalf without any installed libraries, since it only instructs the agent and the training itself runs on the user's machine
Try asking
- “Write a DPO training script for Qwen2.5-0.5B-Instruct using my chosen/rejected preference JSON file”
- “Set up a full RLHF pipeline from SFT to reward model to PPO for a small model”
- “Create a GRPO training loop with a custom reward function that favors concise answers”
- “Explain the hyperparameters like beta and learning rate for DPO training”
Install in 3 steps
- 1
Download the skill
The ZIP contains the 5 original files, the MIT licence and an attribution note.
trl-fine-tuning.zip - 2
Add it to your agent
Claude Code (all projects)
mkdir -p ~/.claude/skills && unzip trl-fine-tuning.zip -d ~/.claude/skills/Claude Code / Agent SDK (this project only)
mkdir -p .claude/skills && unzip trl-fine-tuning.zip -d .claude/skills/Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.
- 3
Ask for the task
No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.
Get better results
- Start with the small Qwen2.5-0.5B models and the provided example datasets to verify your setup before scaling up
- Check the reference files for detailed guidance on DPO variants, online RL, reward modeling, and SFT before committing to a method
- Review any generated training commands and dataset choices yourself, since the skill's examples use specific public datasets and default-style hyperparameters
What's inside
- references/dpo-variants.md
- references/online-rl.md
- references/reward-modeling.md
- references/sft-training.md
- SKILL.md
Redistributed unmodified from Orchestra-Research/AI-Research-SKILLs under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.