trl-training

Train and fine-tune transformer language models via TRL CLI for RLHF workflows.

Updated Aug 27, 2026

Search Tags:#fine-tuning #transformers #dpo #trl #rlhf #sft #grpo

Ready to run

npx skills add https://github.com/JiaTianyu20031016/MIL --skill trl-training

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: trl-training
Source: https://github.com/JiaTianyu20031016/MIL/tree/main/trl/skills/trl-training
Command: npx skills add https://github.com/JiaTianyu20031016/MIL --skill trl-training

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Train and fine-tune transformer language models using TRL to simplify complex RLHF-aligned training workflows and experiments.

Core Features & Use Cases

CLI-driven training for SFT, DPO, GRPO, RLOO, and reward-model training on diverse datasets.
PEFT/LoRA support to enable efficient fine-tuning on large models.
Distributed training with Accelerate for multi-GPU and multi-node setups.
Reproducibility through YAML configuration files to lock in hyperparameters and datasets.

Quick Start

Run a TRL training job using the sft command with your model and dataset to begin fine-tuning.

Frequently Asked Questions about trl-training

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I fine-tune a transformer model using TRL via CLI?▼

To fine-tune a transformer using TRL via CLI, run the appropriate training command like SFT with your target model and dataset. This streamlines RLHF-friendly workflows directly from your terminal.

Can I use PEFT or LoRA for efficient fine-tuning with TRL?▼

Yes, TRL supports PEFT and LoRA to enable efficient fine-tuning on large transformer models. This allows you to train massive models without requiring prohibitive amounts of GPU memory.

Does TRL support distributed training for multi-GPU setups?▼

TRL supports distributed training using Accelerate for multi-GPU and multi-node setups. This allows you to scale SFT, DPO, and GRPO workflows across multiple devices to accelerate training.

What RLHF training workflows are available in TRL?▼

TRL supports SFT, DPO, GRPO, RLOO, and Reward Modeling workflows. These allow you to apply reinforcement learning from human feedback to align transformer language models using diverse datasets and prompts.

How do I ensure reproducibility when fine-tuning models with TRL?▼

You can ensure reproducibility in TRL by using YAML configuration files. These files lock in your hyperparameters and dataset configurations, guaranteeing consistent training runs across different environments.

Do I need Accelerate installed to run TRL training jobs?▼

Accelerate is optional but required for distributed multi-GPU and multi-node training. You need a compatible Python environment with TRL installed and access to datasets to run standard single-device training.