Reasoning models use reinforcement learning (RL) as a method to improve their decision-making processes by learning from the consequences of their actions. In this context, reasoning models seek to solve complex problems by simulating various scenarios and receiving feedback based on their performance. Reinforcement learning provides a framework where an agent interacts with an environment, takes actions, and receives rewards or penalties. This feedback is crucial as it helps the reasoning model adjust its strategies over time to maximize its rewards.
For example, consider a reasoning model applied to automated game play. When the model makes a move in a game, it receives a score based on the outcome of that move. If the model wins, the score (reward) increases, and if it loses, the score decreases (penalty). Through many iterations of playing the game, the model learns which moves tend to lead to higher scores, allowing it to refine its strategies to improve performance. This trial-and-error process where the model learns from previous experiences is fundamental to how reinforcement learning contributes to reasoning models.
In practical applications, RL can also be paired with supervised learning, where reasoning models initially learn from labeled data before fine-tuning their strategies through reinforcement learning. A common example is self-driving cars, where initial training might involve understanding the rules of the road from data, and subsequent training uses reinforcement learning based on real-time driving experiences to optimize actions, such as accelerating, braking, or turning at intersections. This combination helps reasoning models effectively tackle real-world challenges by enabling them to learn and adapt over time.