# Towards Information-Seeking Agents

arXiv: https://arxiv.org/abs/1612.02605 · HTML: https://mlsanity.com/paper/1612.02605/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2016.

## Built on by (most cited first, 4 of 4)

- [ChatShop: Interactive Information Seeking with Language Agents](https://mlsanity.com/paper/2404.09911/) (2024)
- [debug-gym: A Text-Based Environment for Interactive Debugging](https://mlsanity.com/paper/2503.21557/) (2025)
- [PRInTS: Reward Modeling for Long-Horizon Information Seeking](https://mlsanity.com/paper/2511.19314/) (2025)
- [Interactive Machine Comprehension with Dynamic Knowledge Graphs](https://mlsanity.com/paper/2109.00077/) (2021)

## Abstract (arXiv)

We develop a general problem setting for training and testing the ability of agents to gather information efficiently. Specifically, we present a collection of tasks in which success requires searching through a partially-observed environment, for fragments of information which can be pieced together to accomplish various goals. We combine deep architectures with techniques from reinforcement learning to develop agents that solve our tasks. We shape the behavior of these agents by combining extrinsic and intrinsic rewards. We empirically demonstrate that these agents learn to search actively and intelligently for new information to reduce their uncertainty, and to exploit information they have already acquired.