Toward Evaluative Thinking: Meta Policy Optimization with Evolving
  Reward Models
Authors: name: Zae Myung Kim, name: Chanwoo Park, name: Vipul Raheja, name: Dongyeop Kang

Reward-based alignment methods for large language models (LLMs) face two key
limitations: vulnerability to reward hacking, where models exploit flaws in the
reward signal; and reliance on brittle, labor-intensive prompt engineering when
LLMs are used as reward models. We introduce Meta Policy Optimization (MPO), a
framework that addresses these challenges by integrating a meta-reward model
that dynamically refines the reward model's prompt throughout training. In MPO,
the meta-reward model monitors the evolving training context and continuously
adjusts the reward model's prompt to maintain high alignment, providing an
adaptive reward signal that resists exploitation by the policy. This
meta-learning approach promotes a more stable policy optimization, and greatly
reduces the need for manual reward prompt design. It yields performance on par
with or better than models guided by extensively hand-crafted reward prompts.
Furthermore, we show that MPO maintains its effectiveness across diverse tasks,
such as question answering and mathematical reasoning, without requiring
specialized reward designs. Beyond standard RLAIF, MPO's meta-learning
formulation is readily extensible to higher-level alignment frameworks.
Overall, this method addresses theoretical and practical challenges in
reward-based RL alignment for LLMs, paving the way for more robust and
adaptable alignment strategies. The code and models will be publicly shared.