🎮 Reinforcement Learning

📹 ICCV2025 · 7 paper notes

📌 Same area in other venues: 📷 CVPR2026 (25) · 🔬 ICLR2026 (400) · 💬 ACL2026 (46) · 🧪 ICML2026 (110) · 🤖 AAAI2026 (58) · 🧠 NeurIPS2025 (143)

🔥 Top topics: Navigation ×2

Embodied Navigation with Auxiliary Task of Action Description Prediction

DescRL introduces action description generation as an auxiliary task for reinforcement learning-based navigation. By distilling knowledge from pretrained vision-language models to train an ADPredictor, the navigation agent simultaneously produces interpretable action descriptions and achieves improved navigation performance, attaining state-of-the-art results on Semantic Audio-Visual Navigation (SAVNav) and several other tasks.

mDP3: A Training-free Approach for List-wise Frame Selection in Video-LLMs

This paper proposes mDP3, a training-free and model-agnostic video frame selection method that estimates frame similarity in RKHS via a conditional Gaussian kernel, leverages Determinantal Point Processes (DPP) to capture query relevance and list-wise diversity, and models temporal structure via a Markov Decision Process (MDP). Using only 8 input frames, mDP3 significantly outperforms uniform sampling and existing frame selection methods on multiple long-video benchmarks.

NavQ: Learning a Q-Model for Foresighted Vision-and-Language Navigation

This paper proposes NavQ, a foresighted VLN agent that employs a Q-model to predict, in a single forward pass, long-horizon future semantic aggregation features (Q-features) for each candidate action. Combined with an A*-style search strategy, NavQ achieves significant improvements on object-goal navigation benchmarks.

Progressor: A Perceptually Guided Reward Estimator with Self-Supervised Online Refinement

This paper proposes Progressor, a framework that learns task-agnostic reward functions from unannotated videos via self-supervision. It provides dense reward signals by predicting task progress distributions and addresses distribution shift during online RL training through an adversarial push-back strategy.

R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization

This paper proposes R1-Onevision, a framework that converts images into formalized textual representations via a cross-modal reasoning pipeline, combined with a two-stage post-training strategy of SFT followed by rule-based reinforcement learning (GRPO), to significantly enhance multimodal reasoning in vision-language models, surpassing GPT-4o on multiple mathematical reasoning benchmarks.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment

This paper proposes RL-Selector, which introduces the ε-sample cover concept to quantify sample redundancy and formulates data selection as a reinforcement learning problem. A lightweight A2C policy network adaptively optimizes the selection strategy, achieving generalization performance comparable to or surpassing full-data training with significantly fewer samples across multiple benchmark datasets.

RoboFactory: Exploring Embodied Agent Collaboration with Compositional Constraints

This paper introduces the concept of compositional constraints to formalize safety and efficiency requirements in multi-agent embodied collaboration, constructs the first multi-agent manipulation benchmark RoboFactory based on this formalization, and systematically investigates architectures and training strategies for multi-agent imitation learning.