> I can see why someone might say there's overlap between RL and SFT (or semi-supervised FT), but how is "traditional" SFT considered RL? What is not RL then? Are they saying all supervised learning is a subset of RL, or only if it's fine tuning?
Sutton and Barto define reinforcement learning as "learning what to do- how to map situations to actions-- so as to maximize a numerical reward signal". This is from their textbook on the topic.
That's a pretty broad definition. But the general formulation of RL involves a state of the world and the ability to take different actions given that state. In the context of an LLM, the state could be what has been said so far, and the action could be what token to produce next.
But as you noted, if you take such a broad definition of RL, tons of machine learning is also RL. When people talk about RL they usually mean the more specific thing of letting a model go try things and then be corrected based on the observations of how that turned out.
Supervised learning defines success by matching the labels. Unsupervised learning is about optimizing a known math function (for example, predicting the likelihood that words would appear near each other). Reinforcement learning would maximize a reward function that may not be directly known by the model, and it learns to optimize it by trying things and observing the results and getting a reward/penalty.
Sutton and Barto define reinforcement learning as "learning what to do- how to map situations to actions-- so as to maximize a numerical reward signal". This is from their textbook on the topic.
That's a pretty broad definition. But the general formulation of RL involves a state of the world and the ability to take different actions given that state. In the context of an LLM, the state could be what has been said so far, and the action could be what token to produce next.
But as you noted, if you take such a broad definition of RL, tons of machine learning is also RL. When people talk about RL they usually mean the more specific thing of letting a model go try things and then be corrected based on the observations of how that turned out.
Supervised learning defines success by matching the labels. Unsupervised learning is about optimizing a known math function (for example, predicting the likelihood that words would appear near each other). Reinforcement learning would maximize a reward function that may not be directly known by the model, and it learns to optimize it by trying things and observing the results and getting a reward/penalty.