arXiv AI
VAttnPO: Visual Attention-Guided Policy Optimization
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arXiv Computation and Language
Sep 4
Attend to Evidence: Evidence-Anchored Spatial Attention Supervision for Multimodal RLVR
The paper introduces EASE, a method that enhances multimodal reinforcement learning with verifiable rewards (RLVR) by adding visual‑evidence process supervision. EASE transforms annotated evidence regions into smoothed visual‑token targets and uses them to guide attention during RL training, but only on high‑reward trajectories. Experiments on Qwen2.5‑VL‑7B, Qwen3‑VL‑4B, and Qwen3‑VL‑8B show that EASE improves average scores over DAPO by 2.5 to 3.1 points across perception, hallucination, visual math, and multimodal reasoning benchmarks, and diagnostics confirm better alignment of visual attention with annotated evidence.
arXiv Computation and Language
Oct 16
Enhancing Multimodal RLVR with Pairwise Token Contrast and Adaptive Trajectory Confidence
The paper introduces PACE, a method that enhances multimodal reinforcement learning with verifiable rewards (RLVR) by using pairwise token contrast and adaptive trajectory confidence. Pairwise token contrast evaluates each token under four teacher views—combining correct/incorrect hints with image presence or absence—to isolate image-dependent support, while adaptive trajectory confidence normalizes teacher predictions to guide credit redistribution. Experiments demonstrate that PACE outperforms existing approaches on multiple multimodal reasoning benchmarks, achieving top accuracy across three backbones and the Qwen3‑VL‑8B‑Instruct model.
Hugging Face Trending Papers
Jul 15
SIVA-RL: Sensitivity-Invariance Visual Alignment for Multimodal Reinforcement Learning
Reinforcement learning with verifiable rewards (RLVR) drives multimodal reasoning, but answer-level correctness does not guarantee that a vision-language model grounds its predictions in visual evidence. Existing visual-intervention methods contrast policy behavior on original and modified images, yet assign supervision by the type of intervention rather than its observed effect.
arXiv AI
Sep 28
From Reward Signal to Visual Utility: A Controlled Audit of Medical VLM Post-Training
arXiv:2609. 31450v1 Announce Type: cross Abstract: Medical vision-language model (VLM) post-training is commonly evaluated through answer accuracy.
arXiv Computer Vision
Aug 27
V-Rubrics: Visual Faithfulness via Rubric-Based Reinforcement Learning
The paper introduces V‑Rubrics, a reinforcement‑learning framework that evaluates vision‑language model responses by breaking them into atomic propositions and scoring them on Visual Faithfulness, Reasoning Consistency, and Instruction Following. Using a fine‑tuned Qwen3‑VL‑8B‑Instruct model and a newly created 50K‑example V‑Rubrics dataset, the authors demonstrate that rubric‑based GRPO outperforms both a shared SFT baseline and an answer‑only GRPO, especially on knowledge‑oriented and visually grounded reasoning tasks.
arXiv AI
Sep 30
On-Policy Visual Evidence Distillation
arXiv:2609.36838v1 Announce Type: cross Abstract: Visual agents solve problems by interleaving reasoning with image operations, and on-policy distillation (OPD) provides guidance from a strong teache...