OpenRLHF - High-Performance RLHF Training
Quick start
OpenRLHF is a Ray-based RLHF framework optimized for distributed training with vLLM inference acceleration.
Installation:
docker run --runtime=nvidia -it --rm --shm-size="10g" --cap-add=SYS_ADMIN \
-v $PWD:/openrlhf nvcr.io/nvidia/pytorch:25.02-py3 bash
sudo pip uninstall xgboost transformer_engine flash_attn pynvml -y
pip install openrlhf[vllm]
PPO Training (Hybrid Engine):
ray start --head --node-ip-address 0.0.0.0 --num-gpus 8
ray job submit --address="http://127.0.0.1:8265" \
--runtime-env-json='{"working_dir": "/openrlhf"}' \
-- python3 -m openrlhf.cli.train_ppo_ray \
--ref_num_nodes 1 --ref_num_gpus_per_node 8 \
--reward_num_nodes 1 --reward_num_gpus_per_node 8 \
--critic_num_nodes 1 --critic_num_gpus_per_node 8 \
--actor_num_nodes 1 --actor_num_gpus_per_node 8 \
--vllm_num_engines 4 --vllm_tensor_parallel_size 2 \
--colocate_all_models \
--vllm_gpu_memory_utilization 0.5 \
--pretrain OpenRLHF/Llama-3-8b-sft-mixture \
--reward_pretrain OpenRLHF/Llama-3-8b-rm-700k \
--save_path ./output/llama3-8b-rlhf \
--micro_train_batch_size 8 --train_batch_size 128 \
--micro_rollout_batch_size 16 --rollout_batch_size 1024 \
--max_epochs 1 --prompt_max_len 1024 --generate_max_len 1024 \
--zero_stage 3 --bf16 \
--actor_learning_rate 5e-7 --critic_learning_rate 9e-6 \
--init_kl_coef 0.01 --normalize_reward \
--gradient_checkpointing --packing_samples \
--vllm_enable_sleep --deepspeed_enable_sleep
GRPO Training (Group Normalized Policy Optimization):
--advantage_estimator group_norm
Common workflows
Workflow 1: Full RLHF pipeline (SFT → Reward Model → PPO)
Step 1: Train reward model (DPO):
deepspeed --module openrlhf.cli.train_rm \
--save_path ./output/llama3-8b-rm \
--save_steps -1 --logging_steps 1 \
--eval_steps -1 --train_batch_size 256 \
--micro_train_batch_size 1 --pretrain meta-llama/Meta-Llama-3-8B \
--bf16 --max_epochs 1 --max_len 8192 \
--zero_stage 3 --learning_rate 9e-6 \
--dataset OpenRLHF/preference_dataset_mixture2_and_safe_pku \
--apply_chat_template --chosen_key chosen \
--rejected_key rejected --flash_attn --gradient_checkpointing
Step 2: PPO training:
ray start --head --node-ip-address 0.0.0.0 --num-gpus 8
ray job submit --address="http://127.0.0.1:8265" \
-- python3 -m openrlhf.cli.train_ppo_ray \
--ref_num_nodes 1 --ref_num_gpus_per_node 8 \
--reward_num_nodes 1 --reward_num_gpus_per_node 8 \
--critic_num_nodes 1 --critic_num_gpus_per_node 8 \
--actor_num_nodes 1 --actor_num_gpus_per_node 8 \
--vllm_num_engines 4 --vllm_tensor_parallel_size 2 \
--colocate_all_models \
--pretrain OpenRLHF/Llama-3-8b-sft-mixture \
--reward_pretrain ./output/llama3-8b-rm \
--save_path ./output/llama3-8b-ppo \
--micro_train_batch_size 8 --train_batch_size 128 \
--micro_rollout_batch_size 16 --rollout_batch_size 1024 \
--max_epochs 1 --prompt_max_len 1024 --generate_max_len 1024 \
--zero_stage 3 --bf16 \
--actor_learning_rate 5e-7 --critic_learning_rate 9e-6 \
--init_kl_coef 0.01 --normalize_reward \
--vllm_enable_sleep --deepspeed_enable_sleep
Workflow 2: GRPO training (no critic model needed)
Memory-efficient alternative to PPO:
ray job submit --address="http://127.0.0.1:8265" \
-- python3 -m openrlhf.cli.train_ppo_ray \
--advantage_estimator group_norm \
--ref_num_nodes 1 --ref_num_gpus_per_node 8 \
--reward_num_nodes 1 --reward_num_gpus_per_node 8 \
--actor_num_nodes 1 --actor_num_gpus_per_node 8 \
--vllm_num_engines 4 --vllm_tensor_parallel_size 2 \
--colocate_all_models \
--pretrain OpenRLHF/Llama-3-8b-sft-mixture \
--reward_pretrain OpenRLHF/Llama-3-8b-rm-700k \
--save_path ./output/llama3-8b-grpo \
--micro_train_batch_size 8 --train_batch_size 128 \
--micro_rollout_batch_size 16 --rollout_batch_size 1024 \
--max_epochs 1 --bf16 \
--actor_learning_rate 5e-7 \
--init_kl_coef 0.01 --use_kl_loss --kl_estimator k3 \
--normalize_reward --no_advantage_std_norm
Key GRPO parameters:
--advantage_estimator group_norm - Enables GRPO--use_kl_loss - KL loss from GRPO paper--kl_estimator k3 - Loss function (k2 ≈ k1)--no_advantage_std_norm - Disables std normalization
Workflow 3: DPO training (preference optimization)
Simpler alternative without reward model:
deepspeed --module openrlhf.cli.train_dpo \
--save_path ./output/llama3-8b-dpo \
--save_steps -1 --logging_steps 1 \
--eval_steps -1 --train_batch_size 256 \
--micro_train_batch_size 2 --pretrain meta-llama/Meta-Llama-3-8B \
--bf16 --max_epochs 1 --max_len 8192 \
--zero_stage 3 --learning_rate 5e-7 --beta 0.1 \
--dataset OpenRLHF/preference_dataset_mixture2_and_safe_pku \
--apply_chat_template --chosen_key chosen \
--rejected_key rejected --flash_attn --gradient_checkpointing
When to use vs alternatives
Use OpenRLHF when:
- Training large models (7B-70B+) with RL
- Need vLLM inference acceleration
- Want distributed architecture with Ray
- Have multi-node GPU cluster
- Need PPO/GRPO/RLOO/DPO in one framework
Algorithm selection:
- PPO: Maximum control, best for complex rewards
- GRPO: Memory-efficient, no critic needed
- RLOO: Modified PPO with per-token KL
- REINFORCE++: More stable than GRPO, faster than PPO
- DPO: Simplest, no reward model needed
Use alternatives instead:
- TRL: Single-node training, simpler API
- veRL: ByteDance's framework for 671B models
- DeepSpeedChat: Integrated with DeepSpeed ecosystem
Common issues
Issue: GPU OOM with large models
Disable model colocation:
--actor_num_gpus_per_node 8 \
--critic_num_gpus_per_node 8 \
--reward_num_gpus_per_node 8 \
--ref_num_gpus_per_node 8
Issue: DeepSpeed GPU index out of range
Set environment variable:
export RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES=1
Issue: Training instability
Use Hybrid Engine instead of async:
--colocate_all_models \
--vllm_enable_sleep \
--deepspeed_enable_sleep
Adjust KL coefficient:
--init_kl_coef 0.05
Issue: Slow generation during PPO
Enable vLLM acceleration:
--vllm_num_engines 4 \
--vllm_tensor_parallel_size 2 \
--vllm_gpu_memory_utilization 0.5
Advanced topics
Hybrid Engine GPU sharing: See references/hybrid-engine.md for vLLM sleep mode, DeepSpeed sleep mode, and optimal node allocation.
Algorithm comparison: See references/algorithm-comparison.md for PPO vs GRPO vs RLOO vs REINFORCE++ benchmarks and hyperparameters.
Multi-node setup: See references/multi-node-training.md for Ray cluster configuration and fault tolerance.
Custom reward functions: See references/custom-rewards.md for reinforced fine-tuning and agent RLHF.
Hardware requirements
- GPU: NVIDIA A100/H100 recommended
- VRAM:
- 7B model: 8× A100 40GB (Hybrid Engine)
- 70B model: 48× A100 80GB (vLLM:Actor:Critic = 1:1:1)
- Multi-node: Ray cluster with InfiniBand recommended
- Docker: NVIDIA PyTorch container 25.02+
Performance:
- 2× faster than DeepSpeedChat
- vLLM inference acceleration
- Hybrid Engine minimizes GPU idle time
Resources
