Which framework should I use for training transformer language models with reinforcement learning (e.g., GRPO)? Any recommendation?
| Feature | trl (Hugging Face) |
unsloth |
verl (Volcano Engine) |
openrlhf |
|---|---|---|---|---|
| Role in GRPO | Full GRPO framework, implements PPO, DPO, IPO, KTO | Accelerates DPO (not a full GRPO framework) | Full GRPO framework, implements PPO, GRPO, ReMax, DAPO, etc. | Full GRPO framework, implements PPO, DPO, KTO |
| Core Function | Easy, comprehensive RLHF with HF models | Speed up LLM SFT/DPO fine-tuning | Flexible, efficient, production-ready RL training for LLMs | Flexible, scalable, research-oriented RLHF |
| Ease of Use | Very High (Trainer API) | High (easy integration) | Moderate (flexible but extensive feature set) | Moderate (more control) |
| Performance | Good, leverages Accelerate | Excellent (speed & VRAM reduction for DPO) | Excellent (SOTA throughput, scales to hundreds of GPUs) | Very Good, designed for large-scale/distributed |
| Integration | Deeply integrated with Hugging Face ecosystem | Integrates well with HF & trl's DPOTrainer |
Compatible with HF/Modelscope, integrates with FSDP, Megatron-LM, vLLM, SGLang | Uses HF models; often more modular |
| Target Audience | Practitioners, general users, rapid prototyping | Anyone doing DPO/SFT, especially on limited hardware | Researchers, advanced practitioners, production teams needing performance/flexibility | Researchers, power users, large-scale deployments |
