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#GRPO

EVOLVE-VLA: Test-Time Training from Environment Feedback for Vision-Language-Action Models

Intermediate

Zechen Bai, Chen Gao et al.Dec 16arXiv

Robots usually learn by copying many demonstrations, which is expensive and makes them brittle when things change.

#EVOLVE-VLA#test-time training#vision-language-action

Zoom-Zero: Reinforced Coarse-to-Fine Video Understanding via Temporal Zoom-in

Intermediate

Xiaoqian Shen, Min-Hung Chen et al.Dec 16arXiv

Zoom-Zero helps AI answer questions about videos by first finding the right moment and then zooming in to double-check tiny details.

#Grounded Video Question Answering#Temporal Grounding#Coarse-to-Fine

SAGE: Training Smart Any-Horizon Agents for Long Video Reasoning with Reinforcement Learning

Intermediate

Jitesh Jain, Jialuo Li et al.Dec 15arXiv

SAGE is a smart video-watching agent that decides when to answer quickly and when to take multiple steps, just like how people skim or rewind long videos.

#any-horizon reasoning#video agents#temporal grounding

Nemotron-Cascade: Scaling Cascaded Reinforcement Learning for General-Purpose Reasoning Models

Intermediate

Boxin Wang, Chankyu Lee et al.Dec 15arXiv

The paper introduces Nemotron-Cascade, a step-by-step (cascaded) reinforcement learning recipe that trains an AI across domains like alignment, instructions, math, coding, and software engineering—one at a time.

#Cascaded Reinforcement Learning#RLHF#Instruction-Following RL

Differentiable Evolutionary Reinforcement Learning

Intermediate

Sitao Cheng, Tianle Li et al.Dec 15arXiv

This paper introduces DERL, a two-level learning system that automatically builds better reward functions for reinforcement learning agents.

#Differentiable Evolutionary Reinforcement Learning#Meta-Optimizer#Meta-Reward

Toward Ambulatory Vision: Learning Visually-Grounded Active View Selection

Intermediate

Juil Koo, Daehyeon Choi et al.Dec 15arXiv

This paper teaches robots to move their camera to a better spot before answering a question about what they see.

#Active Perception#Embodied AI#Vision-Language Models

QwenLong-L1.5: Post-Training Recipe for Long-Context Reasoning and Memory Management

Intermediate

Weizhou Shen, Ziyi Yang et al.Dec 15arXiv

QwenLong-L1.5 is a training recipe that helps AI read and reason over very long documents by improving the data it learns from, the way it is trained, and how it remembers important stuff.

#long-context reasoning#reinforcement learning#GRPO

DentalGPT: Incentivizing Multimodal Complex Reasoning in Dentistry

Intermediate

Zhenyang Cai, Jiaming Zhang et al.Dec 12arXiv

DentalGPT is a special AI that looks at dental images and text together and explains what it sees like a junior dentist.

#DentalGPT#multimodal large language model#dentistry AI

Rethinking Expert Trajectory Utilization in LLM Post-training

Intermediate

Bowen Ding, Yuhan Chen et al.Dec 12arXiv

The paper asks how to best use expert step-by-step solutions (expert trajectories) when teaching big AI models to reason after pretraining.

#Supervised Fine-Tuning#Reinforcement Learning#Expert Trajectories

Are We Ready for RL in Text-to-3D Generation? A Progressive Investigation

Intermediate

Yiwen Tang, Zoey Guo et al.Dec 11arXiv

This paper asks whether reinforcement learning (RL) can improve making 3D models from text and shows that the answer is yes if we design the training and rewards carefully.

#Reinforcement Learning#Text-to-3D Generation#Hi-GRPO

MOA: Multi-Objective Alignment for Role-Playing Agents

Intermediate

Chonghua Liao, Ke Wang et al.Dec 10arXiv

Role-playing agents need to juggle several goals at once, like staying in character, following instructions, and using the right tone.

#multi-objective alignment#role-playing agents#reinforcement learning

Rethinking Chain-of-Thought Reasoning for Videos

Intermediate

Yiwu Zhong, Zi-Yuan Hu et al.Dec 10arXiv

The paper shows that video AIs do not need long, human-like chains of thought to reason well.

#video reasoning#chain-of-thought#concise reasoning

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