There, areward function isdrawn from one of multiple possible reward models atthebeginning ofeveryepisode, buttheidentity ofthechosen rewardmodel is not revealed to the agent. Hence, the latent state space, for which the dynamics are Markovian, is not given to the agent. We study the problem of learning a near optimal policy for two reward-mixing MDPs. Unlike existing approaches that rely on strong assumptions on the dynamics, we make no assumptions and study the problem in full generality.

While 8-bit floating point formats appeartobesufficient fortraining [14,19],4-bit gradient representations appear challenging from a quantization error (rounding), precision and dynamic range perspective.

This result was later extended to discrete-time iterated function systems by [CDE+21].

To address this, we introduceTASKBENCH, a comprehensive framework to evaluate the capability of LLMs in task automation. Specifically, task automation can be divided into three critical stages: task decomposition, tool selection, and parameter prediction. To tackle the complexities inherent in these stages, we introduce the concept of Tool Graph to represent decomposed tasksandadoptaback-instruct method togenerate high-quality userinstructions. We propose TASKEVAL, a multi-faceted evaluation methodology that assesses LLMperformance across thesethreestages.

Determinantal point processes (DPPs) offer a popular diversification model in machine learning.