> Given a task u \in \mathcal{U} and a human-designed action set \mathcal{A}u with R \in \mathcal{A}u , at time step t , we sample a thought-action pair (h_t, a_t) \sim \pi\theta(a_t \mid \mathcal{A}u, u, c{t-1}) following the ReAct framework (Yao et al., 2023b). Here, c{t-1} = \{(h_1, a_1, o_1), \dots, (h_{t-1}, a_{t-1}, o_{t-1})\} represents the interaction history up to time t-1 . The action a_t is executed, and an observation o_t is returned from the environment, updating the context to c_t = c_{t-1} \cup \{(h_t, a_t, o_t)\} . If a_t contains a new function not present in \mathcal{A}_{t-1}^g , we update the generated action set by setting \mathcal{A}t^g = \mathcal{A}{t-1}^g \cup f(a_t) , where f(a_t) denotes the set of functions defined in action a_t .
This is a roundabout way to say: "We pick an action based on what’s happened so far, do it, see the result, and update the history. If it’s something new, we add it to the list of actions we can use."
There is definitely a certain language and a precise mathematical approach which is needed to pass review for academic papers. It isn't nonsense, but does obfuscate obvious meanings.
This is what chatgpt gave me for the prompt "can you explain this in two sentences". It's pretty close to what you wrote.
> The system follows the ReAct framework to decide on a thought and action at each step based on the task, available actions, and interaction history, updating its context with the results of the action. If the action introduces new functions, the system expands its action set to include these new capabilities.
However, they have been efforts like https://explorer.invariantlabs.ai/benchmarks/ that try to make agents more transparent in that way (show interaction logs).