PPO Clipped Surrogate Objective in RLHF

Advantage Function Estimation in RLHF

PPO Objective Formula for LLM Training in RLHF

Diagnosing Training Instability in Language Model Fine-Tuning

A team is fine-tuning a language model using a reinforcement learning process. In each step, the model generates a response to a prompt, a separate reward model scores the response, and the language model's parameters are updated based on this score. The team finds that a simple update rule, which aggressively maximizes the immediate reward, often leads to 'policy collapse'—the model's linguistic quality degrades, and it starts generating repetitive, nonsensical text that happens to exploit the reward model. What is the primary reason for employing an algorithm like Proximal Policy Optimization (PPO) in this scenario?

When fine-tuning a language model with a reward signal, an optimization method like Proximal Policy Optimization (PPO) is used. A correct implementation of this method would prioritize maximizing the reward score above all else, allowing for significant and unconstrained changes to the model's policy in each training step to quickly find high-reward outputs.