High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.
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OpenRLHF is a Ray-based RLHF framework optimized for distributed training with vLLM inference acceleration.
Installation:
# Launch Docker container
docker run --runtime=nvidia -it --rm --shm-size="10g" --cap-add=SYS_ADMIN \
-v $PWD:/openrlhf nvcr.io/nvidia/pytorch:25.02-py3 bash
# Uninstall conflicts
sudo pip uninstall xgboost transformer_engine flash_attn pynvml -y
# Install OpenRLHF with vLLM
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):
# Same command as PPO, but add:
--advantage_estimator group_norm
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
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 normalizationSimpler 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
Use OpenRLHF when:
Algorithm selection:
Use alternatives instead:
Issue: GPU OOM with large models
Disable model colocation:
# Remove --colocate_all_models flag
# Allocate separate GPUs for each model
--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 # Increase from 0.01
Issue: Slow generation during PPO
Enable vLLM acceleration:
--vllm_num_engines 4 \
--vllm_tensor_parallel_size 2 \
--vllm_gpu_memory_utilization 0.5
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.
Performance:
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).