Workflow:Deepspeedai DeepSpeed Hybrid Engine RLHF Training
| Knowledge Sources | |
|---|---|
| Domains | RLHF, LLMs, Distributed_Training |
| Last Updated | 2026-02-09 00:00 GMT |
Overview
End-to-end process for Reinforcement Learning from Human Feedback (RLHF) training using DeepSpeed's Hybrid Engine, which seamlessly combines inference optimizations with training capabilities.
Description
This workflow covers the complete RLHF training pipeline using DeepSpeed's Hybrid Engine, which enables efficient switching between inference mode (for generating trajectories) and training mode (for policy updates) within the same engine. RLHF requires both high-throughput inference (to generate responses from the policy model) and efficient training (to update the policy using PPO or similar algorithms). The Hybrid Engine applies inference optimizations (kernel injection, tensor parallelism) during generation and automatically switches to full training mode for gradient computation. This workflow also supports LoRA (Low-Rank Adaptation) for parameter-efficient fine-tuning within the RLHF loop.
Usage
Execute this workflow when you need to train a language model using human feedback (RLHF, DPO, PPO) where the training pipeline requires alternating between model inference (for generation/evaluation) and model training (for policy updates). The Hybrid Engine is especially valuable when generation throughput is a bottleneck, as it applies inference-time kernel optimizations that standard training engines cannot use. Use this for training ChatGPT-like assistants, reward model training, and any workflow that mixes inference and training in the same loop.
Execution Steps
Step 1: Supervised Fine-Tuning (SFT)
Perform initial supervised fine-tuning of the base language model on instruction-following data. This produces the starting policy model for RLHF. Use standard DeepSpeed distributed training (ZeRO optimization) for this phase. The SFT model learns to follow instructions and generates coherent responses.
Key considerations:
- Use instruction-tuning format data (prompt/response pairs)
- Standard DeepSpeed ZeRO training workflow applies here
- The SFT checkpoint becomes the starting point for both actor and reference models
- Apply appropriate data formatting with the model's chat template
Step 2: Reward Model Training
Fine-tune a separate reward model on human preference data (pairs of responses ranked by quality). The reward model learns to score responses, providing the reward signal for PPO training. This step also uses standard DeepSpeed training.
Key considerations:
- Reward model can be smaller than the policy model
- Training data consists of preference pairs (chosen vs rejected responses)
- The reward model produces a scalar score for each response
- Use a separate DeepSpeed training run with appropriate configuration
Step 3: Hybrid Engine Initialization
Initialize the DeepSpeed Hybrid Engine for the actor (policy) model by passing enable_hybrid_engine=True to deepspeed.initialize(). The Hybrid Engine wraps the model with inference kernel injection for generation mode and preserves full gradient computation for training mode. Separately initialize the critic model and reference model.
Key considerations:
- The actor model uses DeepSpeedHybridEngine for efficient generation + training
- Critic and reference models can use standard DeepSpeed engines
- Hybrid Engine applies tensor parallelism and kernel injection for inference paths
- LoRA can be applied to the actor model for parameter-efficient updates
Step 4: Experience Generation
Use the Hybrid Engine in inference mode to generate response trajectories from the policy model. The engine automatically applies inference optimizations (fused kernels, optimized attention) during generation. Compute reward scores using the reward model and value estimates using the critic model.
Key considerations:
- Call model.eval() to activate inference optimizations
- Generation uses DeepSpeed's optimized inference kernels automatically
- Batch generation across prompts for throughput
- Collect (prompt, response, reward, value) tuples for PPO training
Step 5: PPO Policy Update
Switch the Hybrid Engine to training mode and perform PPO (Proximal Policy Optimization) updates. The engine automatically deactivates inference optimizations and enables gradient computation. Update both the actor (policy) model and the critic (value) model using the collected experience.
Key considerations:
- Call model.train() to switch to training mode with gradient computation
- PPO computes policy loss with clipped surrogate objective
- Value function loss trains the critic to better estimate returns
- KL divergence penalty prevents the policy from diverging too far from the reference
- Multiple PPO epochs per batch of experience are typical
Step 6: Iteration and Checkpoint
Repeat the experience generation and policy update cycle for multiple iterations. Save checkpoints periodically to preserve training progress. The final model checkpoint represents the RLHF-aligned model ready for deployment.
Key considerations:
- Monitor reward metrics and KL divergence during training
- Save checkpoints at regular intervals
- The reference model is frozen and used only for KL computation
- Training typically requires hundreds to thousands of PPO iterations