Workflow:Allenai Open instruct Reward Model Training
| Knowledge Sources | |
|---|---|
| Domains | LLMs, Reward_Modeling, Post_Training |
| Last Updated | 2026-02-07 00:00 GMT |
Overview
End-to-end process for training a reward model that scores language model responses by quality, used for preference-based reinforcement learning.
Description
This workflow trains a reward model from an SFT checkpoint by adding a scalar value head and optimizing it on preference data. The reward model learns to assign higher scores to human-preferred (chosen) responses and lower scores to rejected alternatives. It uses a Bradley-Terry pairwise ranking loss (logsigmoid of the reward margin). The implementation disables dropout for training stability and initializes the score head with a specific weight distribution.
The entry point is reward_modeling.py, which supports training on mixed preference datasets with Accelerate and DeepSpeed ZeRO Stage 3.
Usage
Execute this workflow when you need a reward model for PPO-style reinforcement learning, or for scoring and ranking model outputs. The reward model is trained on the same preference data used for DPO and takes an SFT model as its starting point. In the Tulu 3 pipeline, the reward model was used for the PPO variant; for GRPO with verifiable rewards, the reward model multiplier is typically set to zero.
Execution Steps
Step 1: Environment_Setup
Prepare the training environment with Accelerate and DeepSpeed. The reward model training uses the same infrastructure as SFT and DPO training, with multi-node support via DeepSpeed ZeRO Stage 3.
Key considerations:
- Typically uses 2 nodes (16 GPUs) for 8B reward models
- Same Docker image and dependency setup as other training workflows
Step 2: Preference_Data_Loading
Load the preference dataset containing chosen/rejected response pairs. The dataset is tokenized with right-padding and filtered by maximum token length. A separate evaluation dataset is loaded for periodic accuracy monitoring.
Key considerations:
- Preference data must contain paired chosen/rejected responses
- The tokenizer pads from the right (different from typical left-padding for generation)
- Both training and evaluation datasets are specified separately
- Maximum token length and prompt token length can be configured independently
Step 3: Model_Initialization
Load the SFT model checkpoint and attach a linear scalar value head for reward scoring. Dropout is disabled throughout the model for training stability. The value head weights are initialized with a specific standard deviation based on the hidden size.
Key considerations:
- The base model is typically an SFT checkpoint (e.g., Llama-3.1-Tulu-3-8B-SFT)
- Dropout is explicitly disabled (following PPO training conventions)
- Score head initialization uses std = 1 / sqrt(hidden_size + 1)
- LigerKernel can be enabled for optimized fused operations
Step 4: Reward_Training
Train the reward model using the Bradley-Terry pairwise ranking loss. For each preference pair, the model scores both the chosen and rejected responses, and the loss encourages the chosen response to have a higher reward. Training metrics include accuracy, reward margins, and per-response reward distributions.
Key considerations:
- Loss function is logsigmoid(chosen_reward - rejected_reward)
- Training accuracy measures how often the model correctly ranks chosen above rejected
- Reward margin tracks the gap between chosen and rejected rewards
- Gradient checkpointing is recommended for memory efficiency
Step 5: Checkpoint_Saving
Save the trained reward model with its value head to a checkpoint. The model can be pushed to HuggingFace Hub for use in downstream PPO or RLVR training.
Key considerations:
- The saved model includes the base model plus the scalar value head
- Auto-upload to HuggingFace Hub is supported via push_to_hub
- The reward model can be referenced via reward_model_path in GRPO/PPO training