Most open online courses on programming makeuse ofautomated grading systems tosupport programming assignments and give real-time feedback. These systems usually rely on test results toquantify theprograms' functional correctness.

Recent advancements in large language models, such as GPT -4, have demonstrated remarkable capabilities in processing standard queries.

Sometraditional models [4,24,25,26,27,29,30] rely on the assumptions and heuristics rules about various measurements of image similarity to perform abstract reasoning.

We give a model of how to infer natural language rules by doing experiments.

Zero-sum games can be solved using online learning dynamics, where a classical technique involves simulating two no-regret algorithms that play against each other and, afterT rounds, the average iterate is guaranteed to solve the original optimization problem with error decaying asO(logT/T). In this paper we show that the technique can be enhanced to a rate ofO(1/T2) by extending recent work [22, 25] that leverages optimistic learning to speed upequilibrium computation.

Neural Information Processing Systems http://nips.cc/

Haussler et al. [1994], Ryabko [2006] researched the online learning problem with a mix of both restrictions. There are also substantial amount of papers investigating online learnability with all measurable functions but restricted data processes.