GeoSolver: Scaling Test-Time Reasoning in Remote Sensing with Fine-Grained Process Supervision

TL;DR

GeoSolver introduces a process-supervised reinforcement learning framework with a large-scale dataset and a token-level reward model to improve reasoning faithfulness and scalability in remote sensing interpretation.

Contribution

It presents GeoSolver, a novel approach combining process supervision, a large dataset, and reinforcement learning to enhance reasoning accuracy and scalability in remote sensing models.

Findings

GeoSolver-9B achieves state-of-the-art results on remote sensing benchmarks.

GeoPRM enables robust test-time scaling and improves general-purpose VLMs.

The framework ensures verifiable, faithful reasoning steps in remote sensing tasks.

Abstract

While Vision-Language Models (VLMs) have significantly advanced remote sensing interpretation, enabling them to perform complex, step-by-step reasoning remains highly challenging. Recent efforts to introduce Chain-of-Thought (CoT) reasoning to this domain have shown promise, yet ensuring the visual faithfulness of these intermediate steps remains a critical bottleneck. To address this, we introduce GeoSolver, a novel framework that transitions remote sensing reasoning toward verifiable, process-supervised reinforcement learning. We first construct Geo-PRM-2M, a large-scale, token-level process supervision dataset synthesized via entropy-guided Monte Carlo Tree Search (MCTS) and targeted visual hallucination injection. Building upon this dataset, we train GeoPRM, a token-level process reward model (PRM) that provides granular faithfulness feedback. To effectively leverage these verification signals, we propose Process-Aware Tree-GRPO, a reinforcement learning algorithm that integrates tree-structured exploration with a faithfulness-weighted reward mechanism to precisely assign credit to intermediate steps. Extensive experiments demonstrate that our resulting model, GeoSolver-9B, achieves state-of-the-art performance across diverse remote sensing benchmarks. Crucially, GeoPRM unlocks robust Test-Time Scaling (TTS). Serving as a universal geospatial verifier, it seamlessly scales the performance of GeoSolver-9B and directly enhances general-purpose VLMs, highlighting its remarkable cross-model generalization.