Image credit: otalk9focus

Reinforcement Learning (RL) is a core pillar of modern AI systems. It mimics how human learn by trying, receiving feedback, and improving over time. (To better understand RL, think of how dogs are trained with rewards and penalties.) Unlike supervised learning, where models learn from labeled datasets, RL allows AI to explore actions, experience outcomes and adjust behavior based on those outcomes. This method is widely used in robotics, game playing agents like Alphago, and even in fine tuning large language model through human feedback. Let’s see the types below:

Types of RL approaches:

1. Positive Reinforcement – The model receives a reward for a correct or desired action, encouraging repetition.
2. Negative Reinforcement- The model avoids actions that result in a negative signal.
3. Punishment based reinforcement- Explicit penalities discourage incorrect behavior, pushing the model to explore better strategies.

Th RL loop is simple but powerful:

Observe the environment-> take an action-> receive a reward or penalty-> update strategy. Over time, the model optimizes its actions to maximize cumulative rewards.

What makes RL even more powerful in today’s AI landascape is the integration of Chain of Thought (CoT) reasoning. Instead of jumping straight to an answer, models are encouraged to think step by step. This structured reasoning is even evaluated as part of the reward mechanism. A response that not only reaches the right answer but also explains it’s path logically is scored higher, enabling models to align more closely with human expectations.

Another key aspect is Reinforcement Learning from Human Feedback (RLHF) widely used to fine tune large language models. Here, AI outputs are evaluated and scored or adjusted by humans, and this feedback becomes part of the reward signal. Over many iterations, the model learns to produce outputs that are more accurate, safe, context aware, and user aligned.

Incorporating inference quality and chain of reasoning into the reward function is helping AI move from surface level response to deeper, more trustworthy outputs. This evolution makes reinforcement learning not just a tool for training agents, but a frameworks for aligning AI behavior with real world expectations-driven by reasoning, feedback and continuous improvement.