Consider two reinforcement learning agents: one playing a chess game and another generating text. Contrast the nature of an 'action' for the chess-playing agent versus the text-generating agent. Specifically, what does a single action entail in each scenario?

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When applying reinforcement learning to Large Language Models, an action, denoted as $$a$$, corresponds to a possible decision the agent can make. Specifically, an action represents a predicted token chosen from the model's vocabulary.

Action in the Context of LLMs

The policy for a Large Language Model is formally expressed as a conditional probability. Specifically, the policy of taking an action $$a$$ in a state $$s$$, denoted as $$\pi(a|s)$$, is the probability of generating the next token $$y_t$$ given the input prompt $$\mathbf{x}$$ and the sequence of previously generated tokens $$\mathbf{y}_{< t}$$. The formula is: $$\pi(a|s) = \Pr(y_t | \mathbf{x}, \mathbf{y}_{< t})$$. In this formulation, the action $$a$$ corresponds to the predicted token $$y_t$$, and the state $$s$$ corresponds to the context sequence $$(\mathbf{x}, \mathbf{y}_{< t})$$.

Policy Formula for LLMs in Reinforcement Learning

A language model is generating a response to the prompt 'The best way to learn a new skill is to...'. So far, it has produced the sequence 'The best way to learn a new skill is to practice'. At this exact point in the generation process, what constitutes the model's next 'action' within a reinforcement learning framework?

Comparing 'Action' in Different Reinforcement Learning Scenarios

Given the following scenario, precisely describe the 'action' the language model has just performed within its reinforcement learning framework.

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