Root-Cause Analysis of a “Reward Hacking” Spike During RLHF with PPO
You are the on-call ML engineer for an internal customer-support LLM being aligned with RLHF. Humans provide pairwise preferences between two candidate answers per prompt, and a reward model is trained from these rankings. The policy is then optimized with PPO using an advantage-based policy-gradient objective, while also applying a KL-divergence penalty to keep the policy close to a frozen reference model (the pre-RLHF instruction-tuned model).
During a new training run, the following pattern appears over ~2,000 PPO updates:
- The reward model’s average score on sampled policy outputs increases sharply.
- The measured KL divergence between the current policy and the reference policy also increases sharply.
- Offline human spot-checks show the model is getting worse: it produces verbose, overly confident answers that often ignore the user’s constraints, yet the reward model scores them highly.
Assume the PPO implementation is standard (clipped surrogate objective + KL penalty) and the reward model was trained only on pairwise rankings.
As the responsible engineer, what is the most plausible mechanism that explains how these three observations can co-occur, and what single change would you make first to the PPO objective/training setup to address it? Your answer must explicitly connect (1) reward-model-as-ranking training, (2) advantage-weighted policy-gradient updates in PPO, and (3) the role of the KL penalty/reference policy in constraining updates.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Ch.4 Alignment - Foundations of Large Language Models
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