Agent skill · AI & Agents

trl-training

Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). Supports SFT, DPO, GRPO, KTO, RLOO and Reward Model training via CLI commands.

Nick44,414★ · +328/wk · 1 repos on radarProfile →
claude-codecodexcursorMIT
Install
npx skills add sickn33/agentic-awesome-skills --skill trl-training --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 9 KB
Bundled scripts: none
Path: skills/trl-training/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 44,414 · +328 this week
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# TRL Training Skill ## When to Use Use this skill when you need train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). Supports SFT, DPO, GRPO, KTO, RLOO and Reward Model training via CLI commands. You are an expert at using the TRL (Transformers Reinforcement Learning) library to train and fine-tune large language models. ## Overview TRL provides CLI commands for post-training foundation models using state-of-the-art techniques: - **SFT** (Supervised Fine-Tuning): Fine-tune models on instruction-following or conversational datasets - **DPO** (Direct Preference Optimization): Align models using preference data - **GRPO** (Group Relative Policy Optimization): Train models by ranking multiple sampled outputs relative to each other and optimizing based on their comparative rewards. - **RLOO** (Reinforce Leave One Out): Online RL training with generation-based rewards - **Reward Model Training**: Train reward models for RLHF TRL is built on top of Hugging Face Transformers and Accelerate, providing seamless integration with the Hugging Face ecosystem. ## Core Commands ### trl sft - Supervised Fine-Tuning Fine-tune language models on instructio

What's inside
Steps it walks through
  1. When to Use
  2. Overview
  3. Core Commands
  4. trl sft - Supervised Fine-Tuning
  5. trl dpo - Direct Preference Optimization
  6. trl grpo - Group Relative Policy Optimization
  7. trl rloo - Reinforce Leave One Out
  8. trl reward - Reward Model Training
  9. Configuration Files
  10. Distributed Training
  11. Troubleshooting
  12. CUDA Out of Memory
  13. Dataset Loading Issues
  14. Model Loading Issues
Commands it runs
trl sft \
trl dpo \
trl grpo \
trl rloo \
trl reward \
trl sft --config sft_config.yaml
trl sft --config sft_config.yaml --learning_rate 1.0e-5
Generate custom config
accelerate config
Use custom config
More from agentic-awesome-skills
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About this skill
What does the trl-training skill do?

Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). Supports SFT, DPO, GRPO, KTO, RLOO and Reward Model training via CLI commands.

How do I install it?

Run `npx skills add sickn33/agentic-awesome-skills --skill trl-training --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From sickn33/agentic-awesome-skills, a repository with 44,414 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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