From Theory to Practice: Implementing RLHF

Reinforcement Learning from Human Feedback (RLHF) has become crucial for aligning language models with human preferences.

Understanding RLHF

RLHF consists of three main components:

  1. Initial supervised fine-tuning
  2. Reward model training
  3. RL fine-tuning

Implementation Steps

1. Supervised Fine-tuning

First, we fine-tune our base model on a high-quality dataset:

def supervised_finetuning(model, dataset):
    optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5)
    
    for batch in dataset:
        outputs = model(batch.input_ids)
        loss = compute_loss(outputs, batch.labels)
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()

2. Reward Model Training

The reward model learns to predict human preferences:

class RewardModel(nn.Module):
    def __init__(self, backbone):
        super().__init__()
        self.backbone = backbone
        self.reward_head = nn.Linear(hidden_size, 1)
    
    def forward(self, x):
        features = self.backbone(x)
        return self.reward_head(features)

3. RL Fine-tuning

Finally, we use PPO to optimize the model using the reward model:

def ppo_training_step(model, reward_model, batch):
    # Generate responses
    responses = model.generate(batch.prompts)
    
    # Compute rewards
    rewards = reward_model(responses)
    
    # PPO optimization
    policy_loss = compute_ppo_loss(responses, rewards)
    policy_loss.backward()

Practical Challenges

  1. Reward Hacking

    • Solution: Careful reward shaping
    • KL penalty to prevent divergence

  2. Training Stability

    • Use value function clipping
    • Proper learning rate scheduling
    • Regular evaluation

  3. Computational Efficiency

    • Gradient checkpointing
    • Mixed precision training
    • Efficient attention implementations

Results and Insights

From my implementation experience: