skills.rest
ZardLi1115
ZardLi1115COMMUNITY
Explore all skills by this author →
GitHub

fine-tuning-with-trl

Align LLMs with TRL trainers for SFT, DPO, PPO, GRPO, and reward modeling.

4|Updated May 18, 2026
Search Tags:#llm#fine-tuning#dpo#trl#rlhf#grpo#reward modeling
One-click install
npx skills add https://github.com/ZardLi1115/zedclaw --skill fine-tuning-with-trl-zardli1115
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/ZardLi1115/zedclaw/tree/main/optional-skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/ZardLi1115/zedclaw --skill fine-tuning-with-trl-zardli1115

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you align large language models (LLMs) with human preferences by using TRL post-training methods like SFT, DPO, PPO, and GRPO instead of relying only on base pretraining.

Core Features & Use Cases

  • SFT for instruction tuning: Train on prompt-completion pairs to improve task-following behavior.
  • DPO for preference alignment: Optimize directly from chosen/rejected preference pairs without a separate reward model.
  • PPO/GRPO for reinforcement learning: Improve outputs using reward signals, including memory-efficient online RL with custom reward functions.
  • Reward modeling support: Train a reward model from preference data to enable stronger RLHF pipelines.
  • Use case: You have a base model and either (a) preference pairs or (b) objective reward signals (format/correctness), and you want an aligned model that follows structured outputs and improves quality with bounded, repeatable training recipes.

Quick Start

Run DPO to align your base instruction model using a chosen/rejected preference dataset by executing: trl dpo --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct --dataset_name trl-lib/ultrafeedback_binarized --output_dir qwen-dpo.

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 align an LLM with human preferences using DPO?▼

DPO aligns an LLM by optimizing directly from chosen and rejected preference pairs without training a separate reward model. You can run it via the TRL CLI using a preference dataset to produce an aligned instruction model.

What is the difference between SFT and DPO for instruction tuning?▼

SFT trains on prompt-completion pairs to improve task-following behavior, while DPO optimizes from chosen and rejected preference pairs for direct preference alignment. SFT establishes baseline instruction following, whereas DPO refines it based on preferences.

Can I use PPO and GRPO for online reinforcement learning in TRL?▼

Yes, TRL supports PPO and GRPO for reinforcement learning using reward signals. GRPOTrainer enables memory-efficient online RL by leveraging custom reward functions to improve model outputs based on objective metrics like format or correctness.

How do I train a reward model from preference data for RLHF?▼

Reward modeling in TRL trains a reward model from preference data using the RewardTrainer. This trained reward model then enables stronger RLHF pipelines by providing the reward signals needed for PPO or GRPO optimization.

Do I need PEFT to run TRL trainers on memory-constrained setups?▼

PEFT acceleration is optional but recommended for memory-constrained setups when running TRL trainers like SFTTrainer or DPOTrainer. It enables efficient fine-tuning of large language models on compatible transformer datasets without exceeding memory limits.

Related Skills

Explore More Education & Research Skills→
SethyPagnaSethyPagna

fine-tuning-with-trl

Fine-tune language models with TRL for post-training alignment workflows.

Community
Advanced
EntelligentsiaEntelligentsia
1

evaluation-harness

Evaluate AI model outputs against predefined criteria using golden datasets and LLM-as-judge.

Official
Advanced
qcmuuqcmuu

fine-tuning-with-trl

Fine-tune language models with TRL for SFT, DPO, PPO, and GRPO workflows.

Community
Advanced
kevinnftkevinnft
13

trl-fine-tuning

Fine-tune LLMs with TRL SFT, DPO, PPO, GRPO, and reward modeling.

Community
Advanced