ECCV2026 Reinforcement Learning AI paper notes paper summaries Multimodal/VLM Agents Object Tracking Federated Learning Biomolecules

🎮 Reinforcement Learning¶

🎞️ ECCV2026 · 12 paper notes

📌 Same area in other venues: 🧠 NeurIPS2026 (1) · 📷 CVPR2026 (25) · 🔬 ICLR2026 (400) · 💬 ACL2026 (46) · 🧪 ICML2026 (110) · 🤖 AAAI2026 (58)

🔥 Top topics: Reinforcement Learning ×6 · Multimodal/VLM ×4

ELVA: Exploring Ranking-Driven Universal Multimodal Retrieval: ELVA proposes a rule-based verifiable reward RL framework that co-optimizes the retrieval-ranking capability of MLLMs through ranking rewards and margin rewards. This addresses the "grain blindness" issue that arises when adapting contrastive learning to retrieval tasks. ELVA achieves SOTA performance on both M-BEIR and a self-built multi-grained benchmark MRBench, with a 13.1% improvement on MRBench.
Teaching an Agent to Sketch One Part at a Time: The paper automatically annotates vector sketches with semantic parts, then trains a vision-language agent with SFT and multi-turn process-reward GRPO to observe the canvas and add curves part by part; users prefer its final outputs over SketchAgent in 77.5% of comparisons.
GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking: GoStop formulates event stream accumulation as a sequential decision-making process governed by a lightweight reinforcement learning agent that dynamically chooses between accumulating events (go) and triggering tracking inference (stop), significantly enhancing tracking accuracy and robustness while substantially lowering latency under abrupt dynamic motions.
FedDO: Dynamic Client Optimization for Adaptive Federated Learning: FedDO formulates client coordination in federated learning as a continuous control problem, leveraging a Distributional Soft Actor-Critic with Three Refinements (DSAC-T) agent to dynamically allocate per-client data-usage ratios and integrating low-rank parameterization to scale to thousands of clients while mitigating gradient drift.
From Illusion to Intention: Visual Rationale Learning for Reliable Evidence Acquisition: To eliminate the disconnect between visual actions and outcomes caused by outcome-only rewards in "thinking with images", this paper proposes Visual Rationale Learning (ViRL), an end-to-end reinforcement learning paradigm that combines evidence-grounded curation, signed-plus-refinement fidelity rewards, and fine-grained credit assignment for faithful and efficient evidence acquisition.
HERO: Enhancing Multimodal Faithfulness via Dynamic Entropy-Aware Reinforcement Learning: Proposes HERO, an on-policy reinforcement learning framework that identifies the "Confidence Trap" in CoT-tuned LVLMs—where models retreat into language priors under visual ambiguity to produce low-entropy confident errors—and overcomes it using dynamic entropy-aware weighting and variance-gated hard-negative sampling.
Molecular Identifier Visual Prompting and Verifiable Reinforcement Learning for Chemical Reaction Diagram Parsing: To resolve the semantic misalignment between visual chemical entities and pre-trained priors, alongside the optimization discrepancy between token-level training and reaction-level evaluation, this paper introduces Identifier as Visual Prompting (IdtVP) and Re3-DAPO verifiable reinforcement learning, substantially enhancing zero-shot parsing accuracy and out-of-distribution robustness across historical scanned literature.
Reinforcement Learning for Multimodal Diffusion Language Models via Bidimensional Trajectory and Thought Optimization: Bi-VRL presents the first bidimensional reinforcement learning framework tailored for multimodal diffusion language models, coupling temporal trajectory-level value estimation across denoising steps with spatial thought-level visual alignment rewards on critical culmination tokens, delivering substantial reasoning gains across both discrete and continuous visual architectures.
Table-MCR2TR: Merged-Cell-Aware Table Recognition via Reinforced Multimodal Language Models: Table-MCR2TR addresses the critical failure of table recognition models on complex merged cells by constructing the large-scale MCT-350K dataset and introducing a progressive multi-task paradigm coupled with GRPO-based collaborative reinforcement learning (M-CRL), enabling a 3B model to achieve state-of-the-art performance and outperform leading proprietary models such as Gemini-2.5-Pro across multiple benchmarks.
TiCRL: Textual Image Classification with Reinforcement Learning-Based Curriculum Learning: TiCRL introduces an OCR-free visual-textual hybrid difficulty measurer and a continuous-action DDPG scheduler that dynamically adjusts sample difficulty thresholds using real-time training feedback, achieving 83.67% accuracy on CNN with 82% data and 93.02% accuracy on DiT with only 58% data.
Unfold The World: Factorize 4D Properties in Reinforcing Spatial Understanding: Addressing the dimensional mismatch where Vision-Language Models collapse 4D physical reality into flat 2D projections, FactoSR factorizes spatial reasoning into orthogonal verifiable rewards across planar correspondence (XY), depth order (Z), and temporal cycle consistency (T), achieving substantial gains of 5.9% on VSI-Bench and 4.5% on All-Angles-Bench.
VPA-WM: Vision-Priors-Aligned World Models for Robust Visual Reinforcement Learning: To overcome the representation collapse of world models under complex visual distractions and resolve the objective mismatch between visual perception and dynamic control, VPA-WM introduces Control-Oriented Alignment (COA) and Spatio-Temporal Collaborative Integration (STCI) to equip world models with both disturbance-robust visual priors and temporally coherent dynamics.