We explore representing complex actions as natural language instructions.

LLMs, but it remains unclear when it helps or hurts.

While contextual bandit has a mature theory, effectively leveraging different feedback patterns to enhance the pace of learning remains unclear.

Then, we rewards (lines Inpractice, we theexplorationrexptdependon policy k recurrent: t, thepolic k(at | (s0,a0,r0,..., st)) conditions states, actions,k.

Each question and answer group has a unique list of corresponding visuals used for image creation. The list of visible objects, which combines the correct-answer objects with an arbitrary'noise' object

In this work, we apply these ideas to sign language recognition.

The classical theory of statistical estimation aims to estimate a parameter of interest under data generated from a fixed design ("offline estimation"), while the contemporary theory of online learning provides algorithms for estimation under adaptively