Tool-augmented large language models (LLMs) leverage tools, often in the form of APIs, to improve their reasoning capabilities on complex tasks. This enables them to act as intelligent agents interacting with the real world. The recently introduced ToolLLaMA model by Qin et al. [ 2023 ] utilizes the depth-first search-based decision tree (DFSDT) mechanism for multi-step reasoning with 16000+ real-world APIs, effectively enhancing the performance of tool-augmented LLMs compared to traditional chain reasoning mechanisms. However, their approach only employs successful paths from decision trees (also called inference trees) for supervised fine-tuning (SFT), missing out on the potential learning opportunities from failed paths. Inspired by this, we propose an inference trajectory optimization framework based on preference learning to address this limitation.

Can incorporating context into self-supervised vision algorithms eliminate augmentation-based inductive priors and enable dynamic adaptation to varying task symmetries? This work suggests a positive answer to this question by proposing to enhance the current joint embedding architecture with a finite context -- an abstract representation of a task, containing a few demonstrations that inform about task-specific symmetries, as shown in Figure 2(c).

Three sampling techniques are presented for comparative investigations.

Understanding the intricate motivations behind human actions is paramount in developing sophisticated systems capable of nuanced behavior analysis.

In this work, we propose data-efficient and interpretable machine learning models for learning to solve numeric planning tasks.

However, their abilities in spatial reasoning, a crucial aspect of human cognition, remain relatively unexplored.

We also demonstrate its effectiveness in generating robotic tasks.