Reinforcement Learning
An agent learns by taking actions and receiving rewards or penalties from its environment.
Reinforcement learning (RL) is a paradigm where an agent observes its environment, takes actions, and receives scalar feedback — rewards for desirable outcomes, penalties for undesirable ones — iterating until it learns a policy that maximizes cumulative reward. Unlike supervised learning, it needs no labeled input-output pairs; the agent explores to discover which actions yield the best long-term returns. On AIF-C01, the key application is reinforcement learning from human feedback (RLHF), used to align large language models with human preferences after pre-training. Expect conceptual questions about RLHF rather than service-specific implementation details.
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