RL and RLM: How AI Learns to Reason

Reinforcement Learning (RL) is a machine learning method in which an agent learns to make decisions through trial and error, receiving rewards for successful actions. This approach has become the foundation for creating reasoning language models (RLM), such as OpenAI’s o1 or Alibaba’s QwQ.

How does RL work? An agent observes the state of the environment, takes an action, receives a reward, and transitions to a new state. This cycle is described by a Markov Decision Process (MDP)—the mathematical foundation of most RL algorithms.

Key Elements of RL

An agent can be a game character, a robot, or a neural network. The environment provides the agent with information (state) and evaluates its actions (reward). The agent’s goal is to maximize cumulative reward, taking into account the discount factor γ (typically between 0.95 and 0.99), which determines how much the agent values long-term rewards.

Application of RL in RLM

In the context of language models, RL allows the model not only to generate text but also to perform logical reasoning. At each step of generation, the model transitions from one state (a portion of the response) to another, receiving a reward for correctness. The PPO (Proximal Policy Optimization) algorithm has become the standard for fine-tuning LLMs, ensuring stable training.

RLMs combine three components:

• Advances in LLMs — foundational language models;
• RL algorithms (e.g., AlphaZero);
• High-performance computing.

Conclusions

Reinforcement learning is a powerful tool that transforms ordinary language models into “reasoning” systems. This approach is already being used in cutting-edge AI products, and its importance will only continue to grow.

Source: Habr