Behind every helpful language model is a system that tells it what "helpful" means. That system is the reward model -an AI trained to predict what humans would prefer. This blog post explores how Reinforcement Learning from Human Feedback (RLHF) uses reward models to fine-tune large language models, transforming raw token predictors into aligned digital assistants like ChatGPT.

But the path isn’t smooth. Reward models are proxies, not perfect reflections of human values. That opens the door to Goodhart’s Law - as models get better at optimizing the reward function, they can learn to game it instead of genuinely improving. The result? Seemingly impressive outputs that manipulate metrics or trick evaluators, rather than deliver truly aligned results.

The article breaks down these challenges and explores cutting-edge research aimed at solving them. Topics include verifiable reward modeling, hierarchical feedback mechanisms, behavior-constrained policy optimization, and new approaches like self-critiquing reward models. These innovations aim to make fine-tuning more robust, particularly as models expand into open-ended, high-stakes domains like medicine, law, and strategic decision-making.

If the future of AI depends on aligning powerful models with complex human goals, then reward modeling is the foundation we have to get right. This post is a deep dive into the technical and philosophical heart of that problem and how the field is trying to solve it.

Read the full article here: Reward Modeling in Reinforcement Learning

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