Reasoning models use reinforcement learning (RL) to improve decision-making by learning from trial and error. These models interact with an environment, take actions, and receive feedback in the form of rewards or penalties. Over time, they optimize their behavior to maximize cumulative rewards. For example, a reasoning model tasked with solving a puzzle might try different sequences of moves, receive a reward for solving it faster, and adjust its strategy based on which actions led to higher rewards. RL algorithms, such as Q-learning or policy gradients, enable the model to balance exploration (trying new strategies) and exploitation (using known effective strategies) to refine its reasoning process.
A concrete example is training a model to play a strategy game like chess. The model starts with random moves but receives positive rewards for checkmating the opponent or capturing pieces. Using RL, it learns to prioritize moves that lead to higher long-term rewards, even if they involve short-term sacrifices. Another example is robotic navigation: a robot learning to navigate a maze receives rewards for reaching the goal and penalties for collisions. The RL framework allows the model to iteratively update its policy—such as a neural network that maps sensor inputs to movement commands—by correlating actions with outcomes. This approach is particularly useful when explicit rules or labeled datasets are unavailable, as the model learns directly from experience.
However, applying RL to reasoning models poses challenges. Sparse rewards—where meaningful feedback is rare—can slow learning. For instance, a model solving complex math problems might only receive a reward after a correct final answer, making it hard to identify which intermediate steps were useful. Techniques like reward shaping (providing intermediate rewards for subgoals) or using actor-critic architectures (which combine policy optimization with value estimation) help address this. Additionally, RL training can be computationally expensive, requiring many iterations. Developers often use simulation environments or curriculum learning (gradually increasing task difficulty) to improve efficiency. Despite these hurdles, RL remains a powerful tool for building reasoning models that adapt dynamically to complex, real-world scenarios.