Training large language models with PPO in the RLHF setting can sometimes feel like navigating a minefield. While powerful, PPO training runs can be sensitive to hyperparameters and implementation details, occasionally leading to instability. Recognizing the symptoms early and knowing how to diagnose and address them is important for successful model alignment. This section provides practical guidance on common instability issues and how to resolve them.

Common Symptoms of Instability

Instability during PPO training often manifests in observable patterns in your training metrics. Keep a close eye on these:

Exploding Rewards: The average reward per episode shoots up rapidly, often far beyond what seems reasonable given the task. This frequently indicates that the policy is exploiting loopholes in the reward model ("reward hacking") rather than genuinely improving alignment.
Collapsing Policy (High KL Divergence): The KL divergence between the trained policy and the reference policy (usually the SFT model) increases drastically and stays high. This means the policy is deviating too much from its initial behavior, potentially losing capabilities learned during pre-training or SFT and generating incoherent text.
Vanishing Gradients / Stagnant Training: Rewards plateau, KL divergence remains very low, and the policy stops improving. This can happen if the learning signal is too weak or if the optimization process gets stuck.
High Value Function Loss: The loss associated with the value network remains high or fluctuates wildly. Since the value function estimates expected future rewards and is used to calculate advantages, an inaccurate value function can destabilize the entire policy update.
Oscillating Performance: Metrics like reward or KL divergence swing back and forth without converging, indicating that the updates might be too large or conflicting.

Diagnostic Tools and Techniques

When you observe signs of instability, systematically diagnose the potential causes:

Monitor Key Metrics: Track these values closely throughout training:
- Mean Reward per Episode/Batch
- KL Divergence ( $D_{KL}$ )
- Policy Loss (PPO objective)
- Value Loss
- Policy Entropy (if applicable, indicates exploration)
- Gradient Norms (for policy and value networks)
Visualizing these metrics over training steps is essential. Look for sudden spikes, collapses, or persistent oscillations.

Rapidly increasing rewards alongside high KL divergence often signals policy collapse or reward hacking.
Inspect Generated Text: Periodically sample responses from the current policy model. Are they coherent? Do high-reward responses actually align with desired behavior, or are they exploiting the reward model (e.g., repetitive phrasing, unnatural style)? Compare them to responses from the initial SFT model.
Analyze Reward Distribution: Look at the distribution of rewards assigned by the reward model. Is it overly skewed? Are there outliers? This can indicate issues with reward model calibration or scaling.
Check Gradients: Monitor the magnitude of gradients during backpropagation. Exploding gradients (very large values) or vanishing gradients (near zero values) point to numerical instability or learning difficulties. Gradient clipping can help mitigate exploding gradients.
Hyperparameter Sensitivity Check: If instability occurs after changing a hyperparameter, revert the change or test intermediate values to understand its impact.

Common Causes and Solutions

Here are typical causes of instability and corresponding solutions:

1. KL Divergence Issues

Cause: The KL penalty coefficient ( $\beta$ ) is too low, allowing the policy to deviate too quickly. Alternatively, the policy updates are too aggressive (high learning rate, large batch size, too many PPO epochs).
Symptoms: High or exploding KL divergence, potentially nonsensical generated text.
Solutions:
- Increase $\beta$ : Strengthen the penalty for deviating from the reference policy. Many implementations use an adaptive KL controller that adjusts $\beta$ based on the observed KL divergence, aiming to keep it within a target range (e.g., 3-10). Tune the target KL value.
- Reduce Learning Rate: Smaller updates make the policy change more gradually.
- Decrease PPO Epochs: Perform fewer optimization steps on each batch of experience, reducing the magnitude of policy change per iteration.
- Use Gradient Clipping: Limit the maximum norm of gradients to prevent excessively large updates.
- Initialize Policy Correctly: Ensure the initial policy for PPO is indeed the SFT model, not the base pre-trained model.

2. Reward Signal Problems

Cause: The reward model is poorly calibrated, noisy, or assigns high rewards to undesirable behavior (reward hacking).
Symptoms: Exploding rewards that don't correlate with actual quality improvements, policy generating repetitive or strange text to maximize score.
Solutions:
- Reward Normalization/Scaling: Standardize rewards per batch (subtract mean, divide by standard deviation) to stabilize their scale. This is often essential.
- Reward Clipping: Limit rewards to a specific range (e.g., [-10, 10]) to prevent extreme values from dominating updates.
- Recalibrate/Retrain Reward Model: If the reward model is fundamentally flawed, revisit Chapter 3. Improve data quality, adjust the training objective, or apply calibration techniques.
- Modify Reward Function: Sometimes, adding terms to the reward function (beyond just the RM score) can help, such as penalties for repetition or length, though this adds complexity.

3. Value Function Instability

Cause: The value network fails to accurately predict the expected returns, leading to poor advantage estimates. This can be due to a high learning rate for the value function, insufficient training, or an inadequate network architecture.
Symptoms: High or fluctuating value loss, oscillating policy performance.
Solutions:
- Tune Value Function Learning Rate: Often requires a separate, potentially higher, learning rate than the policy network.
- Increase Value Function Training Epochs: Train the value network for more steps on each data batch.
- Use Gradient Clipping for Value Loss: Prevent exploding gradients specifically in the value network.
- Check Value Network Architecture: Ensure it's appropriately sized relative to the policy network. Sometimes initializing it from the SFT model (minus the final layer) helps.
- Use Generalized Advantage Estimation (GAE): GAE often provides more stable advantage estimates than simpler methods. Tune the $\lambda$ parameter (typically 0.9-1.0).

4. Implementation and Configuration Errors

Cause: Bugs in the PPO logic (e.g., advantage calculation, KL estimation, gradient updates) or incorrect configuration (e.g., batch sizes, generation settings).
Symptoms: Unpredictable behavior, NaN values in losses, crashes, performance that doesn't match expectations based on similar setups.
Solutions:
- Leverage Established Libraries: Use well-tested libraries like Hugging Face's TRL whenever possible, as they handle many PPO intricacies.
- Code Review and Debugging: Carefully step through your implementation, especially the core PPO update loop, GAE calculation, and KL divergence estimation.
- Unit Tests: Implement tests for individual components of your PPO pipeline.
- Check Batching and Data Handling: Ensure data is correctly batched and processed for both policy and value updates. Pay attention to padding and masking.
- Verify Generation Parameters: Ensure temperature, top_k, top_p used during response generation for PPO training are reasonable and allow for sufficient exploration without producing overly random text.

Troubleshooting PPO often involves iterative experimentation. Change one thing at a time, monitor the effects closely, and leverage the diagnostic tools described above. While achieving stable PPO training for large models can be challenging, understanding these common failure modes and their solutions significantly increases your chances of success in aligning your LLM using RLHF.