Process reward models: a simple explainer

Imagine you're training a puppy. You don't just wait until the end of the day to see if it did its business outside or chewed on the furniture. You give it treats and praise for good behaviour throughout the day, right? This way, the puppy learns what actions lead to positive outcomes.

Similarly, researchers are trying to teach AI systems by rewarding them not just for the final answer, but also for the steps they take to get there. This is where process reward models (PRMs) come in.

PRMs are like trainers for AI systems. They provide feedback at each stage of a task, guiding the AI towards the right solution. This is especially helpful for complex tasks that require multiple steps, like solving a math problem or writing a persuasive essay.

Traditional AI models often struggle with these complex tasks. They might get the final answer right by chance, but they may not understand the underlying process. PRMs help them develop that understanding by rewarding them for taking the correct steps along the way.

Researchers are constantly improving PRMs. Here are some recent advancements:

• More nuanced feedback: New PRMs are being developed that can provide more specific feedback at each step. This helps the AI understand not just if it's on the right track, but also how to get even better.
• Better data collection: Training PRMs requires a lot of data. Researchers are working on ways to collect this data more efficiently and effectively.

PRM research is a rapidly evolving field. With continued development, PRMs have the potential to revolutionise the way AI systems learn and solve problems.

For example:

• OpenAI here discuss a new approach called Process Q-value Model (PQM) that shows promise in giving more accurate feedback at each step.
• Patrick McGuinness here summarises some recent research in this space and explores how PRMs can be used to improve the reasoning abilities of LLMs.
• Nathan Lambert here discusses the challenges of collecting data for training PRMs.