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fine-tuning-with-trl

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

Updated May 4, 2026
Search Tags:#fine-tuning#dpo#trl#rlhf#grpo#llm alignment#reward model
One-click install
npx skills add https://github.com/Junkfooooood/hermes-governance --skill fine-tuning-with-trl-junkfooooood
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/Junkfooooood/hermes-governance/tree/main/skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/Junkfooooood/hermes-governance --skill fine-tuning-with-trl-junkfooooood

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 - Multiple Alignment Methods: Provides workflows for SFT, DPO, PPO, GRPO, and reward model training using HuggingFace TRL trainers. - Deep Reference Guides: Includes detailed documentation on DPO loss variants, GRPO reward function design, online RL methods, and SFT dataset formats. - Production Template: Ships a ready-to-adapt GRPO training script with LoRA, multiple reward functions, and memory-optimized configuration. - Use Case: Train a model on GSM8K math problems with GRPO, rewarding correct answers and structured XML output, using the provided template with parameter-efficient LoRA adapters. ## Quick Start Ask the AI to set up a DPO training run with TRL on a preference dataset using the Qwen2.5 model and recommended hyperparameters.

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 train a model with DPO using TRL?▼

Use DPOTrainer with a preference dataset containing prompt, chosen, and rejected fields. Configure DPOConfig with beta (default 0.1), learning rate around 5e-7, and pass the model, tokenizer, and dataset, then call trainer.train().

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

DPO aligns models directly from preference pairs without a reward model. PPO uses a trained reward model for full RL control but needs more memory. GRPO compares multiple generations per prompt using custom reward functions and is more memory-efficient than PPO.

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 effective batch size. You can also enable gradient checkpointing and use LoRA adapters to cut memory usage.

Why does GRPO loss increase during training?▼

Rising loss is expected in GRPO because it measures KL divergence from the initial policy as the model learns. Monitor reward metrics and reward_std instead of loss to judge training progress.

What dataset format does TRL reward model training require?▼

RewardTrainer expects preference data with prompt, chosen, and rejected fields, such as trl-lib/ultrafeedback_binarized. The model is loaded with num_labels=1 and trained with the Bradley-Terry loss to score chosen above rejected.

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

Use Axolotl for YAML-driven training configuration or Unsloth for fast LoRA fine-tuning when you do not need RLHF. TRL is the right choice when you need preference alignment, reward modeling, or online RL methods like PPO and GRPO.

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