Self-Generating: REWARD VLM (ARMAP) for Test-Time-Scaling

Explore self-generating AI VISION REWARD Models (VLM) with advanced RL (Reinforcement Learning) algos for deep reasoning complex tasks in multi-AI-Agent systems. New AI research.

New robotics algos and insights to optimize "computer use" by AI systems.
With detailed explanations - for beginners and experts. Develop new AI code and configurational reasoning systems.

All rights w/ authors:
ARMAP: SCALING AUTONOMOUS AGENTS VIA AUTOMATIC REWARD MODELING AND PLANNING
Zhenfang Chen, MIT-IBM Watson AI Lab
Delin Chen, UMass Amherst
Rui Sun, University of California, Los Angeles
Wenjun Liu, UMass Amherst
Chuang Gan, UMass Amherst and MIT-IBM Watson AI Lab

Scaling Test-Time Compute Without Verification or RL is Suboptimal
Amrith Setlur, Nived Rajaraman, Sergey Levine and Aviral Kumar
from Carnegie Mellon University and UC Berkeley

Do We Need to Verify Step by Step? Rethinking Process Supervision from a Theoretical Perspective
Zeyu Jia
zyjia@mit.edu
Alexander Rakhlin
rakhlin@mit.edu
Tengyang Xie
tx@cs.wisc.ed

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#vision
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