TRL Training on Hugging Face Jobs

Overview

Train language models using TRL (Transformer Reinforcement Learning) on fully managed Hugging Face infrastructure. No local GPU setup required — models train on cloud GPUs and results are automatically saved to the Hugging Face Hub.

TRL provides multiple training methods:

• SFT (Supervised Fine-Tuning) — standard instruction tuning
• DPO (Direct Preference Optimization) — alignment from preference data
• GRPO (Group Relative Policy Optimization) — online RL training
• Reward Modeling — train reward models for RLHF

See references/training_methods.md for method overviews and selection guidance.

When to Use Unsloth

Use Unsloth (references/unsloth.md) instead of standard TRL when GPU memory is limited (~60% less VRAM), speed matters (~2x faster), training large models (>13B), or training Vision-Language Models (Unsloth has FastVisionModel support). See scripts/unsloth_sft_example.py for a production-ready training script.

Key Directives