Learn What's Left, Not What's Mastered: Saturation Aware Advantage Reweighting for Multi-Reward Policy Optimization Paper • 2608.16072 • Published 11 days ago • 149
Demystifying Agent Skills: Why They Work-Until They Don't Paper • 2608.14036 • Published 14 days ago • 166
Co-RL: Unsupervised Reasoning Emerges from Diverse Cohort in Multi-agent RL Paper • 2608.17253 • Published 9 days ago • 94
Demystifying Agent Skills: Why They Work-Until They Don't Paper • 2608.14036 • Published 14 days ago • 166
Learn What's Left, Not What's Mastered: Saturation Aware Advantage Reweighting for Multi-Reward Policy Optimization Paper • 2608.16072 • Published 11 days ago • 149
From RGB Generation to Dense Field Readout: Pixel-Space Dense Prediction with Text-to-Image Models Paper • 2607.06553 • Published Jul 9 • 20
SPACENUM: Revisiting Spatial Numerical Understanding in VLMs Paper • 2605.23898 • Published May 22 • 7
Reward-Decomposed Reinforcement Learning for Immersive Video Role-Playing Paper • 2605.04733 • Published Jun 3
On-Policy Distillation with Best-of-N Teacher Rollout Selection Paper • 2605.09725 • Published May 13
ThinkJEPA: Empowering Latent World Models with Large Vision-Language Reasoning Model Paper • 2603.22281 • Published Mar 23 • 22
Vision Language Models Cannot Reason About Physical Transformation Paper • 2603.07109 • Published Mar 7 • 2