trl-training Skill
Post-train LLMs with TRL (Transformers Reinforcement Learning) — SFT, DPO, GRPO, KTO, and reward-model training. Use when writing or debugging training code with the TRL Python API or the trl CLI. Published by huggingface in skills.
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Documentation, Data analysis, Writing, Includes SKILL.md
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Copy skill directory
28 days ago · Apache-2.0 license
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What is trl-training Skill?
Post-train LLMs with TRL (Transformers Reinforcement Learning) — SFT, DPO, GRPO, KTO, and reward-model training. Use when writing or debugging training code with the TRL Python API or the trl CLI. Published by huggingface in skills. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement. See how SkillIndex evaluates profiles.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Documentation
- Data analysis
- Writing
- Documentation use cases
- Data analysis use cases
Declared skill metadata
- Declared author: huggingface
- Declared license: Apache-2.0
- Source file: skills/trl-training/SKILL.md
These fields retain source and confidence evidence from the indexed SKILL.md.
Compatibility and setup
- Install or run with Copy skill directory
When to use trl-training Skill
- Use it for documentation.
- Use it for data analysis.
- Use it for writing.
Built with
Editorial notes
Source
- Creator: huggingface
- Repository: huggingface/skills
- Skill file: skills/trl-training/SKILL.md
What it does
Post-train LLMs with TRL (Transformers Reinforcement Learning) — SFT, DPO, GRPO, KTO, and reward-model training. Use when writing or debugging training code with the TRL Python API or the trl CLI.
Skill instructions
TRL Each method pairs a Trainer class with a Config dataclass. Configs extend transformers.TrainingArguments, so all of its arguments work in any trainer config. | Trainer | Dataset type | |---|---| | SFTTrainer | language modeling or prompt-completion | | DPOTrainer | preference (chosen/rejected pairs) | | GRPOTrainer | prompt-only + reward function(s) | | DistillationTrainer | prompt-only + a teacher model (on-policy distillation) | | KTOTrainer | unpaired preference (per-sample bool label) | | RewardTrainer | preference (chosen/rejected pairs); trains a scalar reward model, not a policy | Many more trainers (OnlineDPO, ORPO, CPO, GKD, …) live in trl.experimental with unstable APIs: https://huggingface.co/docs/trl/experimentaloverview python from datasets import loaddataset from trl import SFTConfig, SFTTrainer trainer = SFTTrainer( model="Qwen/Qwen2.5-0.5B", model ID or a PreTrainedModel instance args=SFTConfig(outputdir="Qwen2.5-0.5B-SFT"), traindataset=loaddataset("trl-lib/Capybar
Verified compatibility and discovery
Frequently asked questions
What is trl-training?
trl-training is a open-source AI agent skill with Copy skill directory. Post-train LLMs with TRL (Transformers Reinforcement Learning) — SFT, DPO, GRPO, KTO, and reward-model training. Use when writing or debugging training code with the TRL Python API or the trl CLI.
Who is trl-training best for?
trl-training is best for reusing agent instructions, scripts, and references, documentation workflows, data analysis workflows, writing workflows.
How do I install trl-training?
Install or run trl-training using Copy skill directory. Check trl-training for the latest setup command.
Is trl-training actively maintained?
trl-training may need a closer maintenance check before production use.
Project health auto-fetched from the source repository.
Maintain this resource?
Review this source-backed profile, send a correction with evidence, or link to it from your documentation. Claims verify your relationship to the project; profile facts still require source evidence and editorial review.
