This work proposes MCR-GRPO, a marginal contribution assignment framework that derives box-level credit directly from each sampled response, preserving GRPO's response-level comparison while enabling box-aware optimization of structured multi-object grounding.
Abstract
Multimodal Large Language Models (MLLMs) are increasingly expected to solve structured perception tasks that require visual recognition, language-to-object binding, object cardinality preservation, and precisely localized grounding and segmentation outputs. However, existing group-relative reinforcement learning methods provide only response-level supervision, creating a granularity mismatch for structured multi-object prediction: a single advantage is broadcast to all tokens in a response, without distinguishing individual box contributions. To address this mismatch, we propose MCR-GRPO, a marginal contribution assignment framework that derives box-level credit directly from each sampled response. Specifically, Marginal Contribution Reward (MCR) estimates each predicted box's contribution through a leave-one-out comparison, measuring how the matched set value changes when the box is removed from the response. After within-response normalization, records that improve the set value receive positive credit, while redundant or harmful ones are suppressed. To make marginal attribution stable and informative, we further introduce a Continuous Matched Set Value Evaluator that integrates permutation-invariant matching, count-aware normalization, and graded localization. MCR-GRPO maps normalized box-level marginal advantages to the token spans that generated each box, preserving GRPO's response-level comparison while enabling box-aware optimization of structured multi-object grounding. Experiments across REC, DOD, segmentation, and counting benchmarks show state-of-the-art performance over prior GRPO-based baselines.
The results show that format-valid responses can mask failures to recover the spatial structure required for verifiable visual inference, and that format-valid responses can mask failures to recover the spatial structure required for verifiable visual inference.
This work introduces CVPD (Contrastive Counterfactual Visual Process Distillation), which is the first fully self-contained framework for dense, on-policy, token-level visual self-distillation for MLLMs, and proposes a three-gate Counterfactual Criterion that identifies visual blind spots where zooming into a region changes and sharpens the model's answer distribution.
Shravan Venkatraman, Omkar Thawakar, Ritesh Thawkar et al.· 1 citation
This work proposes to model objects as a stronger semantic unit for visual prediction, encouraging the encoder to learn the global context and semantics among visual elements, and shows that an object-centric objective reduces pixel-averaging shortcuts and yields more globally coherent and context-consistent representations.
This paper explores post-training reinforcement learning (RL), specifically GRPO, to directly align autoregressive perception models with their evaluation metrics, and designs an RL framework that addresses perception-specific challenges: reward design for set-structured outputs and multi-head sampling control.
Sofian Chaybouti, Yasser Dahou, N. Huynh et al.· 0 citations
Generative Vision-Language Models (VLMs) commonly treat bounding-box coordinates as independent output symbols, leaving numerical order and axis semantics implicit. We identify this representation as an important source of error in visual grounding. Hi-Token encodes each coordinate with axis-specific tokens for the hundreds, tens, and ones digits, which adds coarse-to-fine structure and increases token reuse while retaining the existing VLM architecture. Hi-GAR complements this representation with a geometry-based reward for Group Relative Policy Optimization (GRPO), using box overlap and coordinate accuracy at multiple scales. Controlled comparisons under matched training conditions show that Hi-Token improves localization throughout the evaluated IoU range. Hi-GAR further reduces low-overlap predictions and is used only during training. Experiments on three VLM backbones and the RefCOCO family show consistent gains across models and benchmarks. Hi-R1 achieves higher values than strong specialist baselines on most reported metrics. Analyses of token frequency, digit boundaries, object scale, and IoU distributions explain the effects of coordinate representation and reward training. The results show that structured coordinate generation provides an effective approach to generative visual grounding. Project page: https://xyzzzh.github.io/Hi-Token/
Multimodal large language models (MLLMs) can generate fluent visual responses even when the underlying visual prediction is weak, ambiguous, or incorrect. This work presents a retrieval-augmented, reliability-aware selective inference method that evaluates the strength and consistency of visual evidence before a prediction is communicated through a downstream multimodal response. A pretrained ResNet-50 encoder extracts normalized visual embeddings, and FAISS retrieves the top𝑘 reference images from an ImageNet-100 evidence database. Prediction reliability is assessed using retrieval similarity, class-support agreement, evidence margin, entropy-based uncertainty, and an aggregate reliability score. A decision gate then determines whether the prediction should be accepted, presented cautiously, or rejected through abstention or fallback. The selected decision is used to control the final user-facing response generated by the model. Experiments on ImageNet-100 show that the proposed method improves the accuracy of returned predictions from 85.84% to 88.88% at 89.04% coverage. The accepted error rate decreases from 14.16% to 11.12%, corresponding to a 3.04-percentage-point absolute reduction and a 21.48% relative reduction. Expected Calibration Error decreases from 7.40% to 5.87%, while high-reliability wrong predictions decrease from 263 to 226. These results demonstrate the potential of retrieval-derived evidence and selective decision gating as a post-hoc reliability mechanism for controlling visual predictions before they are incorporated into multimodal responses. The current evaluation is conducted in a controlled visual-classification setting and does not constitute a complete assessment of free-form MLLM hallucination.
