Though LLMs are capable of generating plausible programs, it's challenging to interact with the LLMs further to revise the program, especially if the user's specific requirements are different from the initial proposal.

In this work, we investigate whether analogical reasoning can enable in-context composition over composable elements of visual stimuli.

Data poisoning considers cases when an adversary manipulates the behavior of machine learning algorithms through malicious training data.

This paper reveals that the common downstream practice of taking a dot product is only a zeroth-order approximation of the optimization goal, resulting in a loss of information during test-time.

Optimization of parameterized policies for reinforcement learning (RL) is an important and challenging problem in artificial intelligence.

We then demonstrate a lower-bound performance guarantee on policies regularized by the stationary state distribution. In practice, SRPO can be an add-on module to context-based algorithms in both online and offline RL settings.