More with Less: a Large Scale Remote Sensing VLM with a Simple Recipe

arXiv:2607.15942 · cs.CV, cs.LG · Submitted 2026-07-17 · Read on arXiv

Listen

Radio episode about this paper

Transcript

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Today's paper: "More with Less".

Jane: A large-scale remote sensing vision-language model (VLM) can achieve competitive performance across diverse benchmarks without requiring specialized architectural modifications,

Tom: First, who's behind it and why it matters.

Paper summary: Tom: Wow, Jane, I'm really excited to discuss this paper today. It’s called "More with Less: a Large Scale Remote Sensing VLM with a Simple Recipe," and it sounds like they’ve managed to achieve something pretty impressive without having to overhaul the entire architecture.

Jane: I agree, Tom; the title itself suggests a lot about how they approached this problem, focusing on simplicity while achieving broad results. The paper claims that their proposed system can get competitive or state-of-the-art zero-shot performance across quite a wide range of remote sensing benchmarks without needing to change the core VLM structure.

Lu: That architectural constraint is what really catches my eye; building something capable across such diverse tasks just by scaling up the training process seems like a very powerful concept for general AI systems, and I'm curious how they managed that versatility.

Meng: From an engineering standpoint, my main question is about the practical implementation; if it doesn't require architectural novelty, what does that actually mean for deploying these models on real-world remote sensing applications where precision matters?

Lalam: Based on my analysis of the paper's focus, I think the most impactful aspect here is demonstrating that massive data and task diversity can be a more reliable path to capability than constantly designing new model structures. This suggests that we might be able to deploy more robust general models faster.

Tom: Exactly, Lalam; it really shifts the focus away from spending all our time on architectural engineering and puts the emphasis squarely on gathering diverse data and setting up smart task scaling. The abstract makes it clear that this approach is about finding a simple recipe that works well across many different remote sensing capabilities.

Jane: And what’s interesting is how they frame this; instead of building a new model for every specific remote sensing need, they suggest we can adapt a strong general VLM effectively by exposing it to enough diverse supervision and using task-aware rewards during training. It really simplifies the path to building useful remote sensing AI.

Lu: I'm thinking about the mechanism described; they use this single language policy that either answers directly or calls a localization tool for segmentation, which is pretty clever because it’s a flexible way for the model to handle both direct questions and spatial tasks simultaneously.

Paper summary: Meng: That heterogeneous behavior sounds complex to train, though; I wonder how they managed to get the model to switch between those two modes reliably using reinforcement learning across all those different task types.

Lalam: The training setup uses a multi-task reinforcement learning framework with adaptive task rewards that cover everything from multiple-choice VQA to segmentation and detection, which seems like the key ingredient for teaching that flexibility without a fixed structure.

Tom: That adaptive reward mechanism is crucial, Meng; it ensures the model isn't just good at one thing but learns how to optimize its behavior based on the specific output format required for each task. It’s about training it to be versatile rather than specialized from the start.

Jane: It really helps put things into perspective; by rewarding performance across different evaluation criteria, they allow the model to develop that ability to handle both textual and spatial reasoning in a cohesive way. This is a big step toward more holistic AI understanding.

Lu: The results mentioned show consistent gains as the training data scale up, correlating with per-task data diversity, which suggests that feeding it varied supervision across multiple sources helps it generalize better for each specific domain.

Meng: So, if we look at the practical implications, this implies that instead of developing highly specialized models for every new sensor or task type in remote sensing, we could start with a versatile foundation and just feed it a lot of varied data to make it competent.

Lalam: Precisely; the finding that performance improves when domains are backed by three or more heterogeneous sources is significant because it shows that complexity in the supervision actually helps drive better generalization for those specific areas.

Tom: That’s what makes this paper compelling, Lalam; it backs up the idea that data scale and task diversity are central drivers of performance in remote sensing VLMs, moving away from chasing architectural novelty. It really validates a datacentric approach to AI development here.

Jane: And when we look at the overall message of "More with Less: a Large Scale Remote Sensing VLM with a Simple Recipe," it’s about showing that you don't need exotic new components to tackle complex, real-world problems like understanding Earth observation data effectively.

Paper summary: Lu: It opens up so many avenues for creative applications; imagine applying this recipe to entirely new domains where we have abundant but highly diverse visual data and need a general reasoning engine to interpret it.

Meng: From an engineering standpoint, the paper flags that tool-specific components like SAM3 should be adapted carefully because fine-tuning them on narrow subsets can reduce domain gains for the sake of better zero-shot performance in other areas. That’s a necessary caution when we move this into production systems.

Lalam: That limitation is important; it tells us that while the VLM foundation scales well, we still need to be mindful of how much we narrow down those specific segmentation tools if we want to maintain high performance in those granular spatial tasks.

Tom: So, what's the big picture here, Jane? If this recipe works, what does it mean for how we build the next generation of remote sensing AI systems across the globe?

Jane: It means we can start building powerful general-purpose models that are then fine-tuned or adapted efficiently using massive, varied supervision to become experts in specific remote sensing applications without needing a completely new foundational model design.

Lu: I see this as a way to democratize access to high-level remote sensing understanding; instead of requiring huge engineering teams to build bespoke models for every niche, anyone with large amounts of diverse data and the right training framework can achieve state-of-the-art results.

Meng: For my team, this suggests we should prioritize building a robust general model first, focusing on massive data curation and setting up those adaptive task rewards rather than immediately chasing the next architectural tweak. It’s a more sustainable engineering path.

Lalam: And for me, from a culture perspective, this reinforces the idea that deep knowledge transfer through diverse tasks is powerful; it shows that learning through varied experiences is a very strong way to build resilient and broadly capable AI systems.

Tom: It really feels like the focus has shifted from pure model innovation to sophisticated data curation and training strategy, which makes sense given the complexity of remote sensing imagery. This paper provides a solid roadmap for how to proceed in this area without getting bogged down in unnecessary architectural complexity.

Conclusion: Tom: So, to wrap up what we’ve been discussing, this paper proposes that you don't need massive architectural changes to build a powerful remote sensing vision-language model if you focus on scaling up the data and the tasks it handles.

Jane: That's right, Tom; the core idea is that by making the training data much bigger and more varied across different remote sensing tasks, we can get these models to perform really well without reinventing the wheel architecturally.

Lu: And what’s fascinating about that "simple recipe" they mention is how it leverages a single language policy with a tool invocation system instead of trying to build a dozen specialized modules. It opens up some really interesting avenues for how we can adapt this foundation across entirely new types of Earth observation data.

Meng: From an engineering standpoint, I’m thinking about the practical application; if we follow this path, it suggests that deploying a highly adaptable general model becomes much more feasible for real-world systems because the training process itself is scalable and less dependent on bespoke hardware configurations.

Lalam: I think what this paper really emphasizes is how improving the sheer scale and heterogeneity of supervision directly translates into better generalization for those models, which I see as a significant step toward building AI that can handle a much wider range of cultural and scientific domains effectively.

Tom: Exactly, Lalam; it’s not just about bigger numbers; it’s about making sure those numbers represent different kinds of problems so the model learns how to reason across them all.

Jane: And the authors, by focusing on this data-centric approach, are pushing back against the idea that every complex problem requires a custom architecture from scratch. It’s really about finding a more sustainable path forward for building useful AI systems in this field.

Lu: I'm really excited about how this could apply to things we haven't even thought of yet; imagine using this recipe to interpret complex, multi-sensor data streams where the relationship between visual and textual information is incredibly intricate.

Meng: I’m still focused on the practical side, though; while the concept is exciting, how do we actually ensure that this simple recipe maintains high performance when we move from a controlled training environment to messy, real-world satellite imagery?

Lalam: The implication for culture is huge because if we can build these versatile models efficiently, it means advanced remote sensing analysis and interpretation tools become accessible to more people globally.

Tom: That’s the big picture, Lalam; this paper lays a clear roadmap for moving toward more general and broadly applicable AI in the remote sensing space by prioritizing data diversity over architectural novelty.

INSAIT, Sofia University

cs.CV, cs.LG

Submitted: 2026-07-17

Updated: 2026-10-01

Comments: ACCV 2026. Project Page https://github.com/insait-institute/MLRS

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 89/100

The gist: A large-scale remote sensing vision-language model (VLM) can achieve competitive performance across diverse benchmarks without requiring specialized architectural modifications, provided it is

Key concepts

More with Less Remote Senser (MLRS)
A vision-language model that operates without adding new architecture. It uses one language policy that chooses between answering questions directly in text or invoking a localization tool for tasks like segmentation and grounding.
Multitask Reinforcement Learning Framework
The training method used to teach the model. It involves training the model across many different remote sensing tasks (like VQA, captioning, detection) using adaptive rewards that consider different output formats and evaluation criteria.
Data Scale and Task Diversity
The central finding: performance in remote sensing VLMs is driven more by having a large, varied dataset spanning many tasks than by complex architectural designs. Increasing data scale and the heterogeneity of tasks lead to better generalization.
Adaptive Reward Mechanism (GRTO)
A reward function used during fine-tuning that jointly optimizes the language policy and the localization tool. It balances format validity (F) and task-specific scores (S) to ensure the model learns to produce correct outputs for different spatial tasks.

Terminology

Summary

A large-scale remote sensing vision-language model (VLM) can achieve competitive performance across diverse benchmarks without requiring specialized architectural modifications, provided it is trained at sufficient scale across varied data and tasks. The core finding suggests that data scale is more important than architectural novelty for remote sensing VLMs, shifting the emphasis from architecture engineering toward data and task scaling.

How it works

The proposed model, More with Less Remote Senser (MLRS), operates without introducing any new model architecture; it utilizes a single language policy that can either answer directly in text or invoke a localization tool for segmentation and grounding. This heterogeneous behavior is trained using a multitask reinforcement learning framework with adaptive task rewards covering multiple-choice VQA, free-form VQA, captioning, detection, and segmentation across a large variety of input types. The model communicates with the segmentation tool only through text: it either answers directly or writes a valid localization call that can be parsed into SAM prompts.

Training Data Scaling and Diversity

The central experiment investigates what drives generalization in multi-task RS training by assembling a mixture spanning all task types and input modalities, where the number and heterogeneity of sources backing each domain varies. The researchers curated an 80k training set from it [a 2.3M raw pool], balancing task domain and question difficulty. They found that performance improves with increased data scale and task diversity, which correlates with per-task data diversity. Specifically, for domains backed by three or more heterogeneous sources, the gains continue to improve throughout training.

Adaptive Reward Mechanism

The model is fine-tuned using Group Relative Tool Optimization (GRTO) to jointly adapt the language policy and the SAM3 localization tool. The reward function is structured as:

R = λfmt F + λtask S, where F is a binary format-validity term, S is a task-specific score, and λfmt = 0.1 and λtask = 0.9 during training. This ensures the model optimizes across tasks with different output formats and evaluation criteria, using metrics like box IoU for detection or mask IoU for segmentation.

Scaling Effects on Performance

The study analyzes how performance evolves across training steps, revealing domain-specific trajectories. For instance, multi-view VQA, backed by a single source, plateaus at +4%, while temporal VQA, backed by only two closely related sources, peaks at 1k steps and then declines below the base model. The analysis of covariates shows that source diversity contributes positively to gains in the multivariate model.

Conclusion

The MLRS model demonstrates that a strong general-purpose VLM can be effectively adapted to compete with or surpass specialized VLMs in the remote sensing domain through large-scale multi-task reinforcement learning. The results indicate that data scale and task diversity are central drivers of remote sensing VLM performance, supporting a datacentric alternative to increasingly specialized architectural design. Furthermore, tool-specific components like SAM3 should be adapted carefully, as fine-tuning them on narrower subsets can trade in domain gains for reduced zero-shot generalization. The overall recipe points toward building general remote sensing VLMs by starting from a strong general-purpose model, scaling diverse remote sensing supervision, and using simple tool interfaces to extend the model to spatial tasks.

The gist: A large scale remote sensing vision-language model can achieve competitive or state-of-the-art zero-shot performance across a wide range of remote sensing benchmarks without modifying the underlying VLM architecture. The core finding suggests that data scale is more important than architectural novelty for remote sensing VLMs, shifting the emphasis from architecture engineering toward data and task scaling.

Improvements for AI systems

Based on the provided research paper, here are specific improvements for AI systems, focusing on shifting from architectural specialization to data-centric scaling and task diversity:

)Architectural Shift (More with Less):

The primary improvement is the shift away from developing new remotesensing-specific architectures towards leveraging a generally capable vision-language model (like InternVL or LLaVA) and scaling it through data, tasks, and reinforcement learning.

  1. Utilize a single, strong general VLM backbone (e.g., InternVL3.5-8B) without modifying the core architecture for remote sensing adaptation.

  2. Employ a unified language policy that handles all input types (textual answering vs. invoking localization tools via SAM3) through a single interface, eliminating the need for separate task routers or specialized encoders/fusion modules.

)Training Strategy & Data Diversity:

The improvement lies in training regimens that maximize data composition and task variety rather than just increasing total sample volume.

  1. Implement a Balanced Multi-task Training Mix (as seen in Figure 1c), ensuring the model is exposed to a wide variety of input types (multi-modal, multi-temporal, ultra-high-res) and tasks (detection, segmentation, VQA).

  2. Use Reinforcement Learning with adaptive task rewards that cover multiple output formats (e.g., binary format validity for tool calls and semantic IoU for segmentation masks) to optimize the model across heterogeneous tasks simultaneously.

  3. Prioritize Source Diversity over sheer volume as the primary driver of generalization gains, especially in source-poor domains (like Temporal VQA), where sustained improvement is only achieved with sufficient heterogeneity in training sources.

)Tool Integration & Segmentation:

The system should be augmented with a tool-use mechanism that decouples semantic understanding from pixel-level localization.

  1. Integrate a segmentation head (via Group Relative Tool Optimization, GRTO) that allows the VLM to decide whether to generate a direct text answer or invoke an external localization tool (like SAM3).

  2. The VLM's role is explicitly defined: semantic grounding and coarse spatial localization via textual prompts, while the external tool handles dense mask generation based on those prompts.

)Specific Capabilities of the Improved System (MLRS):

The resulting system—the More with Less Remote Senser (MLRS)—can perform the following specific functions:

  1. Multi-modal Understanding: Process and reason over diverse remote sensing inputs, including Optical, False-colour, and SAR imagery.

  2. Temporal Reasoning: Analyze multi-image sequences to understand dynamic changes over time (e.g., change detection).

  3. High-Resolution Analysis: Perform reasoning on ultra-high-resolution imagery (up to 10,000x10,000px) for detailed visual question answering and reasoning.

  4. Precise Spatial Localization: Accurately locate and segment objects in remote sensing scenes by invoking a specialized localization tool (SAM3) guided by language instructions (e.g., identifying the road or the building).

  5. Flexible Output Generation: Seamlessly switch between generating natural language answers for VQA/captioning and producing structured, pixel-level segmentation masks for tasks like object counting or shape recognition, all from a single policy.

Sources

Related papers