Learning to Think and Explore in 3D World

Yijiang Li, Bingyang Wang, Nuno Vasconcelos1 min readHugging FaceOct 15, 2026

Key Takeaway for Students

We study how vision-language models (VLMs) can learn to think and actively explore in 3D space. Instead of providing multi-view images, videos, or 3D representations to the model, we preserve the native visual encoding of VLMs, i.e., a single image, one at a time, just like humans, and treat the 3D representation only as an environment with which the model can interact through

53.2% that22.0% andsurpasses proprietary Gemini 3

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This research explores how VLMs can actively interact with and explore 3D environments using single-image observations, mimicking human perception. This approach is crucial for developing more embodied and intelligent AI agents capable of complex reasoning and navigation in real-world scenarios.

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Career Insight

high demand

Why it matters

Skills to learn

Implement reinforcement learning algorithms (e.g., PPO, SAC) for VLM agentsDesign reward functions for interactive AI agents in 3D environmentsIntegrate VLMs with 3D simulation environments (e.g., Habitat, AI2-THOR)Perform trajectory-supervised fine-tuning for initial agent behaviorDevelop strategies for efficient rendering and observation generation in 3D for VLMs

Interview questions

Describe how you would design a reinforcement learning pipeline for a vision-language model to navigate and answer questions in a 3D environment, given only single-image observations.
What are the key challenges in training a VLM to perform active exploration and reasoning in a 3D space, and how would you address issues like sparse rewards or catastrophic forgetting?
Propose a system architecture for deploying a VLM-based 3D exploration agent that can handle real-time interaction, including rendering, action prediction, and state management, for multiple concurrent users.

Relevant for ~24 months · generated by the enrichment pipeline