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fine-tuning-with-trl

Fine-tune language models with SFT, DPO, PPO, GRPO, and reward modeling using TRL.

Updated Jun 5, 2026
Search Tags:#fine-tuning#dpo#trl#rlhf#grpo#llm alignment#reward modeling
One-click install
npx skills add https://github.com/xu1713/openhorse --skill fine-tuning-with-trl-xu1713
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/xu1713/openhorse/tree/main/openhorse/openhorse/optional-skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/xu1713/openhorse --skill fine-tuning-with-trl-xu1713

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 configure and debug from scratch. ## Core Features & Use Cases - Full RLHF Pipeline: Run the complete SFT → Reward Model → PPO workflow with step-by-step checklists and working code for each stage. - Preference Alignment with DPO: Train on chosen/rejected pairs without a reward model, with 10+ loss variants (IPO, hinge, robust, APO) and tuned hyperparameters. - Memory-Efficient GRPO: Train with custom reward functions using group-relative optimization, including a production-ready template and deep guidance on reward design, mode collapse, and loss behavior. - Use Case: Fine-tune Qwen2.5-1.5B on GSM8K with GRPO using correctness and format reward functions to produce structured reasoning outputs with XML-tagged answers. ## Quick Start Ask the AI to set up a DPO training run with TRL on a preference dataset using the provided configuration and trainer code.

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 DPO using TRL?▼

Load a preference dataset with prompt, chosen, and rejected fields, configure DPOConfig with beta and learning rate, then train with DPOTrainer passing the model, tokenizer, and dataset. A CLI alternative is available via the trl dpo command.

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

SFT trains on prompt-completion pairs for instruction following. DPO aligns with preference pairs without a reward model. PPO uses a trained reward model for maximum control. GRPO runs memory-efficient online RL by comparing multiple generations per prompt.

How much GPU memory does TRL training require?▼

For 7B models, SFT needs about 16GB with LoRA, DPO about 24GB since it stores a reference model, PPO about 40GB for policy plus reward model, and GRPO about 24GB. LoRA, gradient checkpointing, and gradient accumulation reduce memory further.

Why does loss increase during GRPO training?▼

Rising loss during GRPO training is expected 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 to 512, and increase gradient_accumulation_steps to maintain the effective batch size. Enabling gradient checkpointing on the model also 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, LitGPT for minimal educational fine-tuning, and Unsloth for fast LoRA training. TRL is the choice when you need RLHF, preference alignment, or reward modeling.

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