trl-fine-tuning_skill

This skill guides fine-tuning LLMs with TRL for instruction tuning, preference alignment, and reward-based optimization, aligning models to human feedback.

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Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill orchestra-research/ai-research-skills --skill trl-fine-tuning

SKILL.md11.2 KB

Overview

This skill packages a practical TRL-based fine-tuning workflow to align large language models with human preferences using SFT, DPO, PPO/GRPO, and reward model training. It targets practitioners who need RLHF-style pipelines with Hugging Face Transformers and supports memory-optimized online RL. The content focuses on actionable steps, checkpoints, and common troubleshooting tips.

How this skill works

The skill inspects and wires together standard TRL components: supervised fine-tuning (SFT) for instruction following, Direct Preference Optimization (DPO) for preference alignment, reward model training for scalar feedback, and PPO/GRPO for policy optimization. It shows dataset formats, configuration knobs, and CLI/python examples to run each stage and to chain them into a full RLHF pipeline. It also highlights memory and stability strategies like LoRA, gradient checkpointing, and KL/beta tuning.

When to use it

You need to align a model with human preference data (chosen/rejected pairs).
You want a full RLHF pipeline: SFT → reward model → PPO.
You prefer DPO for direct preference optimization without building a reward model.
You have memory constraints and need online RL with GRPO.
You must train or evaluate a reward model to score generations.

Best practices

Start with SFT on high-quality instruction pairs before RL to stabilize policy.
Use DPO when you only have preference pairs and want a simpler pipeline than reward‑model + PPO.
Apply LoRA or QLoRA, gradient checkpointing, and smaller per-device batches to reduce VRAM usage.
Tune KL/beta and learning rates iteratively; higher beta/KL keeps the model conservative.
Validate reward models: check loss behavior and ensure chosen examples clearly beat rejected ones.

Example use cases

Full RLHF: SFT on instruction data, train a reward model from preferences, then run PPO to maximize reward scores.
Preference-only alignment: train with DPO using chosen/rejected pairs to directly bias a model toward preferred outputs.
Memory-limited online RL: use GRPO with a custom reward function for efficient online updates.
Reward model development: train a sequence classifier to score prompt+completion pairs and use it for downstream RL.
Quick experiments: use the CLI scripts (trl dpo, trl grpo, trl.scripts.ppo) for rapid prototyping.

FAQ

DPO expects records with prompt, chosen, and rejected text fields (prompt + two completions). You can load JSON or HF datasets in that layout.

How do I avoid OOM errors during training?

Lower per-device batch size, reduce max sequence length, enable gradient_checkpointing, or use LoRA/QLoRA and gradient accumulation to maintain effective batch size.

When should I use GRPO instead of PPO?

Choose GRPO when GPU memory is limited or when you prefer online, memory-efficient policy updates; use PPO when you need finer RL control and can afford more VRAM.