Synchronizing Models During Training

As we integrate the components of the RLHF pipeline, particularly during the Proximal Policy Optimization (PPO) phase, managing the interactions and states of multiple large language models becomes a significant engineering task. The PPO step doesn't just involve the policy model we are actively tuning; it also relies on a reward model (RM), a value model (critic), and often a static reference model. Ensuring these models are correctly loaded, utilized, and updated (or kept static) is essential for stable and effective training.

The Cast of Models in PPO

During the RL optimization stage, several models operate concurrently:

1. Policy Model (Actor): This is the LLM whose parameters are being actively updated by the PPO algorithm. It generates responses to prompts, and its goal is to maximize the reward signal provided by the RM while staying close to its initial distribution (often the SFT model).
2. Value Model (Critic): This model estimates the expected return (cumulative future reward) from a given state (prompt or prompt + partial generation). It's trained alongside the policy model to help reduce the variance of policy gradient estimates, typically by computing advantages. It's often initialized from the policy model or the RM architecture but trained with a different objective (e.g., minimizing mean squared error against observed returns). Its parameters are also updated during PPO.
3. Reward Model (RM): Trained in the previous phase on human preference data, this model provides the scalar reward signal for generated responses. Crucially, the RM's parameters are frozen during the PPO phase. It acts as a fixed judge of response quality based on learned human preferences.
4. Reference Policy Model: This is typically a copy of the SFT model before PPO fine-tuning begins. Its role is fundamental in calculating the KL divergence penalty term in the PPO objective: $L_{PPO} = L_{\text{clip}} - c_{\text{vf}} L_{\text{VF}} + c_{\text{entropy}} S[\pi_{\theta}] + \beta \text{KL}[\pi_{\theta} || \pi_{\text{ref}}]$. The KL term, $\beta \text{KL}[\pi_{\theta} || \pi_{\text{ref}}]$, penalizes the policy $\pi_{\theta}$ for deviating too far from the reference policy $\pi_{\text{ref}}$. Like the RM, the parameters of the reference model are kept frozen throughout PPO training to provide a stable target distribution.

Ensuring Correct Model States

Maintaining the correct state for each model, especially distinguishing between those being trained and those held static, requires careful implementation.

• Freezing Weights: Both the Reward Model and the Reference Policy Model must have their parameters frozen. In frameworks like PyTorch, this is typically achieved by setting the requires_grad attribute of their parameters to False before passing them or the policy/value model parameters to the optimizer. This ensures that no gradients are computed or applied to these static models during backpropagation.

  # Example (PyTorch)
  reward_model.eval() # Set to evaluation mode
  reference_model.eval() # Set to evaluation mode
  for param in reward_model.parameters():
      param.requires_grad = False
  for param in reference_model.parameters():
      param.requires_grad = False
  
  # Policy and Value models remain trainable
  policy_model.train()
  value_model.train()
  
  # Optimizer typically only includes policy and value model parameters
  optimizer = torch.optim.Adam(
      list(policy_model.parameters()) + list(value_model.parameters()),
      lr=learning_rate
  )
  

• Loading and Instantiation: The workflow must correctly load the final SFT model to serve as the initial policy and the reference model. Similarly, the trained reward model must be loaded. The value model might be initialized from the policy model's weights or another strategy.

Synchronization in Distributed Training

Training large models like those used in RLHF often necessitates distributed computing setups across multiple GPUs or even multiple machines. This introduces further synchronization challenges:

• Parameter Consistency: All distributed workers must start with the exact same initial parameters for the policy, value, reference, and reward models. This usually involves loading checkpoints consistently across all processes.

• Gradient Aggregation: During PPO updates, gradients calculated on different batches of data by different workers need to be aggregated before updating the policy and value models. Standard distributed training libraries (like torch.distributed or deepspeed) provide mechanisms like AllReduce to average gradients across all workers. This ensures that all workers apply the same update, keeping the policy and value models synchronized.

  Diagram illustrating model synchronization in a distributed PPO setting. Each worker computes gradients locally using shared fixed Reward and Reference models. Gradients for the Policy and Value models are aggregated (e.g., via AllReduce) before a synchronized update is applied to these models across all workers.

• Static Model Access: The fixed reward and reference models might be replicated on each worker, or techniques like parameter server architectures could be used, though replication is common for inference-only models. The key is that every worker must access an identical, unchanging version of these models throughout the PPO phase.

Failure to synchronize correctly can lead to divergent model behavior across workers, stale gradients, and ultimately, unstable or ineffective training. Libraries like Hugging Face's TRL abstract away some of these details, particularly the handling of the reference model and KL calculations, but understanding the underlying synchronization requirements remains important, especially when debugging or customizing the pipeline. Careful management of model states and communication protocols in distributed settings is therefore not just an implementation detail, but a prerequisite for successful RLHF training at scale.