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All SourcesarXiv
#Reinforcement Learning

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

SPARK: Stepwise Process-Aware Rewards for Reference-Free Reinforcement Learning

Intermediate
Salman Rahman, Sruthi Gorantla et al.Dec 2arXiv

SPARK teaches AI to grade its own steps without needing the right answers written down anywhere.

#SPARK#Process Reward Model#PRM-CoT

ReVSeg: Incentivizing the Reasoning Chain for Video Segmentation with Reinforcement Learning

Intermediate
Yifan Li, Yingda Yin et al.Dec 2arXiv

ReVSeg teaches an AI to segment objects in videos by thinking step-by-step instead of guessing everything at once.

#Reasoning Video Object Segmentation#Vision-Language Models#Temporal Grounding
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