Reinforcement Learning

In reinforcement learning, an agent observes a state, takes an action, receives a reward signal, and updates its behavior policy to maximize future rewards — without being given explicit labeled examples of correct behavior. This differs fundamentally from supervised learning: instead of learning from a fixed dataset of input-output pairs, the RL agent learns from the consequences of its own actions in an environment. The key components are the policy (the agent's decision-making function), the reward model (the signal defining what "good" looks like), and the environment (the world the agent interacts with).

In the LLM context, Reinforcement Learning from Human Feedback (RLHF) is how frontier models like GPT-4 and Claude are aligned to be helpful, harmless, and honest. Human raters compare model outputs and signal preferences; these preferences train a reward model; and then the LLM is fine-tuned using Proximal Policy Optimization (PPO) to produce outputs that score highly according to the reward model. More recent variants replace expensive human raters with AI feedback (RLAIF) or use direct preference optimization (DPO) to simplify the training pipeline while achieving comparable alignment quality.

Beyond LLM alignment, RL drives measurable business value in process optimization domains: supply chain routing that reduces logistics costs by 15–30%, dynamic pricing that maximizes revenue under demand uncertainty, and data center cooling systems (like Google's DeepMind-optimized facilities) that reduce energy consumption by 40%. The common thread is sequential decision-making under uncertainty where the reward signal can be quantified — the same mathematical framework applies whether the agent is a language model learning to write helpful responses or a routing algorithm learning to minimize delivery delays.