On the Out-of-Distribution Generalization of Reasoning in Multimodal LLMs for Simple Visual Planning Tasks
arXiv:2602.15460v1 Announce Type: new Abstract: Integrating reasoning in large language models and large vision-language models has recently led to significant improvement of their capabilities. However, the generalization of reasoning models is still vaguely defined and poorly understood. In this work, we present an evaluation framework to rigorously examine how well chain-of-thought (CoT) approaches generalize on a simple planning task. Specifically, we consider a grid-based navigation task in which a model is provided with a map and must output a sequence of moves that guides a player from a start position to a goal while avoiding obstacles. The versatility of the task and its data allows us to fine-tune model variants using different input representations (visual and textual) and CoT reasoning strategies, and systematically evaluate them under both in-distribution (ID) and out-of-distribution (OOD) test conditions. Our experiments show that, while CoT reasoning improves in-distrib
arXiv:2602.15460v1 Announce Type: new Abstract: Integrating reasoning in large language models and large vision-language models has recently led to significant improvement of their capabilities. However, the generalization of reasoning models is still vaguely defined and poorly understood. In this work, we present an evaluation framework to rigorously examine how well chain-of-thought (CoT) approaches generalize on a simple planning task. Specifically, we consider a grid-based navigation task in which a model is provided with a map and must output a sequence of moves that guides a player from a start position to a goal while avoiding obstacles. The versatility of the task and its data allows us to fine-tune model variants using different input representations (visual and textual) and CoT reasoning strategies, and systematically evaluate them under both in-distribution (ID) and out-of-distribution (OOD) test conditions. Our experiments show that, while CoT reasoning improves in-distribution generalization across all representations, out-of-distribution generalization (e.g., to larger maps) remains very limited in most cases when controlling for trivial matches with the ID data. Surprisingly, we find that reasoning traces which combine multiple text formats yield the best (and non-trivial) OOD generalization. Finally, purely text-based models consistently outperform those utilizing image-based inputs, including a recently proposed approach relying on latent space reasoning.
Executive Summary
This article presents an evaluation framework to examine the generalization of reasoning models in multimodal large language models (LLMs) for simple visual planning tasks. The authors fine-tune model variants using different input representations and chain-of-thought (CoT) reasoning strategies, and systematically evaluate them under in-distribution (ID) and out-of-distribution (OOD) test conditions. The results show that CoT reasoning improves ID generalization, but OOD generalization remains limited. Surprisingly, reasoning traces combining multiple text formats yield the best OOD generalization. The study highlights the importance of OOD generalization in multimodal LLMs and suggests potential applications in tasks requiring robust reasoning and planning.
Key Points
- ▸ The authors present a rigorous evaluation framework for the generalization of reasoning models in multimodal LLMs.
- ▸ CoT reasoning improves in-distribution generalization across all representations.
- ▸ Out-of-distribution generalization remains limited in most cases when controlling for trivial matches with the ID data.
- ▸ Reasoning traces combining multiple text formats yield the best OOD generalization.
Merits
Strength in methodology
The authors provide a comprehensive evaluation framework that systematically examines the generalization of reasoning models under various conditions, making a significant contribution to the field.
Novel findings
The study reveals surprising results regarding the importance of combining multiple text formats for OOD generalization, which challenges existing assumptions and offers new insights for future research.
Demerits
Limited context
The study focuses on a specific task (grid-based navigation) and may not be representative of more complex planning tasks, which could limit the generalizability of the findings.
Need for further exploration
The authors acknowledge that OOD generalization remains a significant challenge and suggest further research to address this issue, highlighting the need for continued investigation in this area.
Expert Commentary
This study provides a significant contribution to the field of multimodal large language models by examining the generalization of reasoning models under various conditions. The authors' evaluation framework offers a comprehensive and systematic approach to understanding the strengths and limitations of CoT reasoning in LLMs. The surprising results regarding the importance of combining multiple text formats for OOD generalization highlight the need for continued research in this area. The study's implications for transfer learning, reasoning, and planning in AI systems are far-reaching and warrant further exploration.
Recommendations
- ✓ Future research should focus on developing more robust and generalizable multimodal LLMs that can account for OOD generalization in a variety of domains and tasks.
- ✓ The study's findings suggest that researchers should prioritize the development of models that can effectively combine multiple text formats for improved OOD generalization.
