Entropy Is Not Enough: Unlocking Effective Reinforcement Learning for Visual Reasoning via Vision-Anchored Token Selection

Problem
This work addresses a significant gap in the application of token-level entropy for credit assignment in reinforcement learning (RL) within visual reasoning contexts. While token entropy has proven effective in text-only RL with verifiable rewards (RLVR), its efficacy diminishes in visual reasoning scenarios due to the exclusion of vision-sensitive tokens that exhibit low entropy. The authors highlight that existing multimodal RL approaches fail to adequately interleave perceptual grounding with semantic reasoning, often neglecting systematic visual measurements or relying solely on token entropy for semantic exploration. This paper is a preprint and has not undergone peer review.

Method
The authors propose VEPO (Vision-Entropy token-selection for Policy Optimization), a novel RL framework that integrates visual sensitivity with token entropy through a multiplicative coupling mechanism. VEPO redirects gradient credit towards tokens that are both visually grounded and informative, thereby enhancing the learning process. The architecture leverages a combination of visual perception and semantic reasoning, allowing for more effective credit assignment in visual reasoning tasks. The training process involves a dataset that includes visual inputs, although specific details regarding the dataset and training compute are not disclosed.

Results
VEPO demonstrates superior performance compared to a baseline that utilizes token entropy alone. In experiments, VEPO achieves an improvement of 2.28 points at a 7B parameter scale and 3.15 points at a 3B parameter scale on relevant benchmarks. These results indicate a significant enhancement in the model’s ability to perform visual reasoning tasks, showcasing the effectiveness of the proposed method in addressing the limitations of traditional entropy-based approaches.

Limitations
The authors acknowledge that their approach may still be limited by the quality and diversity of the visual datasets used for training, which could affect generalization to unseen scenarios. Additionally, the paper does not explore the computational efficiency of VEPO compared to other state-of-the-art methods, which could be a critical factor in practical applications. The reliance on a multiplicative coupling mechanism may also introduce complexity that could hinder scalability in larger models.

Why it matters
The introduction of VEPO has significant implications for the field of reinforcement learning, particularly in tasks requiring visual reasoning. By effectively combining visual sensitivity with token entropy, this framework paves the way for more robust multimodal RL systems that can better understand and interact with complex visual environments. The findings encourage further exploration of hybrid approaches that integrate perceptual and semantic elements in RL, potentially leading to advancements in AI applications such as robotics and autonomous systems. This work is available on arXiv.