# Gold Seeker: Information Gain from Policy Distributions for Goal-oriented Vision-and-Langauge Reasoning

arXiv: https://arxiv.org/abs/1812.06398 · HTML: https://mlsanity.com/paper/1812.06398/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2018.

## Built on by (most cited first, 5 of 5)

- [Unified Questioner Transformer for Descriptive Question Generation in Goal-Oriented Visual Dialogue](https://mlsanity.com/paper/2106.15550/) (2021)
- [What Does The User Want? Information Gain for Hierarchical Dialogue Policy Optimisation](https://mlsanity.com/paper/2109.07129/) (2021)
- [SCONE-GAN: Semantic Contrastive learning-based Generative Adversarial Network for an end-to-end image translation](https://mlsanity.com/paper/2311.03866/) (2023)
- [Bayesian Low-Rank LeArning (Bella): A Practical Approach to Bayesian Neural Networks](https://mlsanity.com/paper/2407.20891/) (2024)
- [Learning to Reason and Navigate: Parameter Efficient Action Planning with Large Language Models](https://mlsanity.com/paper/2505.07500/) (2025)

## Abstract (arXiv)

As Computer Vision moves from a passive analysis of pixels to active analysis of semantics, the breadth of information algorithms need to reason over has expanded significantly. One of the key challenges in this vein is the ability to identify the information required to make a decision, and select an action that will recover it. We propose a reinforcement-learning approach that maintains a distribution over its internal information, thus explicitly representing the ambiguity in what it knows, and needs to know, towards achieving its goal. Potential actions are then generated according to this distribution. For each potential action a distribution of the expected outcomes is calculated, and the value of the potential information gain assessed. The action taken is that which maximizes the potential information gain. We demonstrate this approach applied to two vision-and-language problems that have attracted significant recent interest, visual dialog and visual query generation. In both cases, the method actively selects actions that will best reduce its internal uncertainty and outperforms its competitors in achieving the goal of the challenge.