fine-tuning-with-trl
This skill teaches post-training techniques for aligning language models to human preferences. It covers supervised fine-tuning, direct preference optimization (DPO), and online reinforcement learning methods like RLOO and GRPO, with complete workflows and practical examples.
fine-tuning-with-trl teaches you to align language models with human preferences using DPO, RLOO, and GRPO methods.
AI-generated summary based on this skill's SKILL.md
Decision gist · record as of 2026-07-28
fine-tuning-with-trl teaches you to align language models with human preferences using DPO, RLOO, and GRPO methods. This skill teaches post-training techniques for aligning language models to human preferences. It covers supervised fine-tuning, direct preference optimization (DPO), and online reinforcement learning methods like RLOO and GRPO, with complete workflows and practical examples.
Use it when
- fine-tuning-with-trl teaches the full RLHF workflow: starting with supervised fine-tuning (SFT) on instruction data.
- Yes.
Verify before relying
Read SKILL.md below before installing (7 files). Open directory: indexed for reading, not audited.
Install
NousResearch/hermes-agent/trl-fine-tuning · repository language: Python
Open directory. Skills are indexed for reading, not audited. Review a skill's body before installing it.
Frequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
What does fine-tuning-with-trl teach about DPO for LLM alignment?
fine-tuning-with-trl covers Direct Preference Optimization (DPO) as a key method for aligning language models with human preferences. DPO trains models directly on preference pairs without requiring a separate reward model, making it more efficient than traditional RLHF. The skill includes practical examples and implementation guidance for applying DPO to your models.
How do I implement a complete RLHF pipeline using fine-tuning-with-trl?
fine-tuning-with-trl teaches the full RLHF workflow: starting with supervised fine-tuning (SFT) on instruction data, then training a reward model to score outputs based on preference data, and finally running online RL using methods like RLOO or GRPO. The skill provides step-by-step guidance and code examples for each stage.
Can fine-tuning-with-trl help me train a reward model?
Yes. fine-tuning-with-trl covers reward model training to score language model outputs based on human preference data. This is a critical component of preference alignment pipelines. The skill explains how to prepare preference datasets and train models that effectively distinguish between better and worse outputs.
What alignment methods does fine-tuning-with-trl compare?
fine-tuning-with-trl covers SFT (supervised fine-tuning), DPO (direct preference optimization), RLOO (online reinforcement learning), and GRPO (group relative policy optimization). The skill helps you choose between these methods based on your use case, considering factors like computational efficiency, data requirements, and alignment quality.
How does fine-tuning-with-trl address training instability and memory issues?
fine-tuning-with-trl includes troubleshooting guidance for common preference alignment challenges like out-of-memory errors, poor output quality, and training instability. The skill provides practical solutions and best practices to help you debug and optimize your alignment training workflows.
What is the license for fine-tuning-with-trl?
fine-tuning-with-trl is released under the MIT license, allowing free use, modification, and distribution for both commercial and personal projects.
SKILL.md
Rendered from the published skill. Quoted content, verbatim.
TRL - Transformer Reinforcement Learning
Quick start
TRL provides post-training methods for aligning language models with human preferences.
Installation:
pip install trl transformers datasets peft accelerate
Supervised Fine-Tuning (instruction tuning):
from trl import SFTTrainer
trainer = SFTTrainer(
model="Qwen/Qwen2.5-0.5B",
train_dataset=dataset, # Prompt-completion pairs
)
trainer.train()
DPO (align with preferences):
from trl import DPOTrainer, DPOConfig
config = DPOConfig(output_dir="model-dpo", beta=0.1)
trainer = DPOTrainer(
model=model,
args=config,
train_dataset=preference_dataset, # chosen/rejected pairs
processing_class=tokenizer
)
trainer.train()
Common
(truncated - see the full file via the links below)
File tree — 7 files
optional-skills/mlops/training/trl-fine-tuning/SKILL.md
optional-skills/mlops/training/trl-fine-tuning/references/dpo-variants.md
optional-skills/mlops/training/trl-fine-tuning/references/grpo-training.md
optional-skills/mlops/training/trl-fine-tuning/references/online-rl.md
optional-skills/mlops/training/trl-fine-tuning/references/reward-modeling.md
optional-skills/mlops/training/trl-fine-tuning/references/sft-training.md
optional-skills/mlops/training/trl-fine-tuning/templates/basic_grpo_training.py
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Related skills
Fine-tuning with TRL provides post-training methods to align language models with human preferences through multiple approaches. Train models on instruction data with SFT, optimize for preference alignment via DPO without a separate reward model, or run full RLHF pipelines combining supervised fine-tuning, reward modeling, and PPO optimization. GRPO offers memory-efficient online reinforcement learning for resource-constrained setups.
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