Aug 2026· Frontiers of Computer Science· 0 citations· 66 references
TL;DR
It is demonstrated that LLMs speed agent learning and greatly reduce the human effort required to achieve robust, reliable, and repeatable task performance.
Abstract
Interactive Task Learning (ITL) enables cognitive agents to learn novel tasks (in one shot) from natural-language instruction and allows humans to customize agents to align with individual preferences. ITL relies on reasoning over and learning from multiple sources of knowledge, a strength of cognitive architectures. However, ITL requires frequent human input, which can be tedious and time-consuming. We evaluate large language models (LLMs) as an additional source of knowledge for ITL. We summarize initial experiments exploring the potential use of LLMs in ITL and then describe a novel method (STARS) that markedly improves the reliability of task learning from LLMs for embodied ITL agents. We demonstrate that LLMs speed agent learning and greatly reduce the human effort required to achieve robust, reliable, and repeatable task performance.
This research benchmarks human evaluations against a large language model (LLM) using a multi-agent approach and/or retrieval-augmented generation (RAG) to automate complex content analysis tasks to leverage artificial intelligence’s efficiency and precision alongside humans’ contextual understanding and domain expertise.
Xinyu Fu, Chaosu Li· Journal of Planning Educatio...· 1 citation
A novel RAIE taxonomy along four scaling dimensions is proposed, which optimizes the entire thought process through search algorithms and self-verification, and introduces a task-oriented guideline for choosing the best TTS strategy.
Jia-Yu An, Zheng Chen, Yongcheng Jing et al.· 0 citations
River, a simple training recipe that improves reward quality by filtering low-quality environments and augmenting outcome rewards with process-level behavior regularization is proposed, which achieves the best performance among evaluated open-source RL-trained 8B models across four terminal-agent benchmarks.
Yi-Fan Yao, Bo Pang, Xuan-Phi Nguyen et al.· 1 citation
The Intelligent Prompt Construction Framework (IPCF), which equips an autonomous agent with the ability to dynamically generate task-specific prompts, and achieves performance gains over existing baselines on the OK-VQA and A-OKVQA datasets.
Zhongjian Hu, Peng Yang, Dong-Mei Yang et al.· ACM Transactions on Multimed...· 0 citations
This work proposes that LLM web agents can learn simple environment observations at test time, and introduces trial steps for agents to decompose a complex environment observation into sub-modules, and implements a label-free learning method, Test-Time Environment Decomposition (TTED), to adapt agent behaviors with experience during inference.
Jun-Xuan Li, Zijun Liu, Zi-Yi Huang et al.· 0 citations
This work instantiate parametric learning via prefix-tuning and augment an LLM to ingest both prefix weights and rich textual data which capture relationships to a target capability, and synthesizes these inputs to perform instruction-steered parametric synthesis, directly outputting new prefix weights that manifest the target skill.
Lucio M. Dery, Benedict Aaron Tjandra, Siavash Samiei et al.· arXiv.org· 0 citations
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