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fine-tuning-with-trl

Fine-tune and align language models using TRL's SFT, DPO, PPO, and GRPO trainers.

Updated Jun 7, 2026
Search Tags:#fine-tuning#HuggingFace#TRL#RLHF#GRPO#dpo#llm alignment
One-click install
npx skills add https://github.com/Chensihakniroth/ANAKOT-AGENT --skill fine-tuning-with-trl-chensihakniroth
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/Chensihakniroth/ANAKOT-AGENT/tree/main/optional-skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/Chensihakniroth/ANAKOT-AGENT --skill fine-tuning-with-trl-chensihakniroth

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires trl, transformers, datasets, peft, accelerate, torch, and includes references (resource) components.

What problem does it solve? Aligning language models with human preferences requires complex post-training pipelines involving supervised fine-tuning, reward modeling, and reinforcement learning, which are difficult to implement correctly from scratch. ## Core Features & Use Cases - Full RLHF Pipeline: Run the complete SFT → Reward Model → PPO workflow to align a base model with human preferences. - Preference Alignment with DPO: Train on chosen/rejected pairs without needing a separate reward model, with 10+ loss variants including IPO, hinge, and robust DPO. - Memory-Efficient Online RL with GRPO: Define custom reward functions for verifiable tasks like math or structured output, with a production-ready training template. - Use Case: Fine-tune Qwen2.5-1.5B-Instruct on GSM8K with GRPO using correctness and format reward functions to teach structured reasoning with XML-tagged answers. ## Quick Start Fine-tune Qwen/Qwen2.5-0.5B on the trl-lib/Capybara dataset using TRL's SFTTrainer with one training epoch.

Frequently Asked Questions about fine-tuning-with-trl

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

FAQPage Schema
How do I fine-tune an LLM with TRL SFTTrainer?▼

Load a model with AutoModelForCausalLM, prepare a prompt-completion or conversational dataset, configure SFTConfig with output directory and learning rate, then pass everything to SFTTrainer and call train(). LoRA can be added via peft_config for parameter-efficient training.

What is the difference between DPO, PPO, and GRPO in TRL?▼

DPO aligns models directly on preference pairs without a reward model. PPO uses a trained reward model for maximum control over reinforcement learning. GRPO compares multiple generations per prompt using custom reward functions and is more memory-efficient than PPO.

How much GPU memory does TRL training require?▼

For a 7B model, SFT needs about 16GB VRAM with LoRA, DPO needs 24GB since it stores a reference model, PPO needs 40GB for policy plus reward model, and GRPO needs around 24GB. Gradient checkpointing and 8-bit optimizers reduce memory further.

Why does loss increase during GRPO training?▼

Rising loss during GRPO training is expected behavior because the loss measures KL divergence from the initial policy as the model learns. Monitor reward metrics and reward_std instead of loss to track actual training progress.

How do I fix OOM errors during DPO training?▼

Reduce per_device_train_batch_size to 1, lower max_length from 1024 to 512, and increase gradient_accumulation_steps to maintain effective batch size. Enabling gradient checkpointing on the model also significantly reduces memory usage.

When should I use Axolotl or Unsloth instead of TRL?▼

Use HuggingFace Trainer for basic fine-tuning without RL, Axolotl for YAML-based training configuration, and Unsloth for fast LoRA training. TRL is the right choice when you need RLHF, preference alignment, reward modeling, or online RL methods.

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