RewardMap: Tackling Sparse Rewards in Fine-grained Visual Reasoning via
  Multi-Stage Reinforcement Learning
Authors: name: Sicheng Feng, name: Kaiwen Tuo, name: Song Wang, name: Lingdong Kong, name: Jianke Zhu, name: Huan Wang
Organization: Westlake University

Fine-grained visual reasoning remains a core challenge for multimodal large
language models (MLLMs). The recently introduced ReasonMap highlights this gap
by showing that even advanced MLLMs struggle with spatial reasoning in
structured and information-rich settings such as transit maps, a task of clear
practical and scientific importance. However, standard reinforcement learning
(RL) on such tasks is impeded by sparse rewards and unstable optimization. To
address this, we first construct ReasonMap-Plus, an extended dataset that
introduces dense reward signals through Visual Question Answering (VQA) tasks,
enabling effective cold-start training of fine-grained visual understanding
skills. Next, we propose RewardMap, a multi-stage RL framework designed to
improve both visual understanding and reasoning capabilities of MLLMs.
RewardMap incorporates two key designs. First, we introduce a difficulty-aware
reward design that incorporates detail rewards, directly tackling the sparse
rewards while providing richer supervision. Second, we propose a multi-stage RL
scheme that bootstraps training from simple perception to complex reasoning
tasks, offering a more effective cold-start strategy than conventional
Supervised Fine-Tuning (SFT). Experiments on ReasonMap and ReasonMap-Plus
demonstrate that each component of RewardMap contributes to consistent
performance gains, while their combination yields the best results. Moreover,
models trained with RewardMap achieve an average improvement of 3.47% across 6
benchmarks spanning spatial reasoning, fine-grained visual reasoning, and
general tasks beyond transit maps, underscoring enhanced visual understanding
and reasoning capabilities.