ARES: Multimodal Adaptive Reasoning via Difficulty-Aware Token-Level
  Entropy Shaping
Authors: name: Shuang Chen, name: Yue Guo, name: Yimeng Ye, name: Shijue Huang, name: Wenbo Hu, name: Haoxi Li, name: Manyuan Zhang, name: Jiayu Chen, name: Song Guo, name: Nanyun Peng

Recent advances in multimodal large reasoning models (MLRMs) have
substantially improved their ability to solve complex textual and visual tasks.
However, these models tend to overthink on simple problems, producing
unnecessarily lengthy reasoning traces, while under-exploring on challenging
ones, leading to missed solutions. To address this imbalance, we propose ARES,
a unified open-source framework for adaptive reasoning that dynamically
allocates exploration effort based on task difficulty. Our approach is
motivated by two key empirical findings: (i) while single-token entropy is
noisy, high window-entropy (HWE) tokens (token-level entropies averaged under a
sliding window) can reliably capture reasoning-critical moments; and (ii)
reducing HWE usage benefits easy problems, while increasing it is essential for
solving hard ones. Building on these insights, ARES introduces a two-stage
training pipeline. In the Adaptive Cold-Start stage, we curate multimodal and
textual data paired with reasoning traces of length proportional to problem
difficulty, equipping the model with initial difficulty awareness. In the
second stage, we develop Adaptive Entropy Policy Optimization (AEPO), which
uses HWE tokens as exploration triggers to decide when to explore, and a
hierarchical entropy reward with dynamic KL control to decide how much to
explore. Extensive experiments demonstrate that ARES achieves superior
performance and reasoning efficiency across diverse mathematical, logical, and
multimodal benchmarks, while closing the gap to leading commercial systems
under significantly lower inference costs.