Tech-Time Crunch with Jai Patel on Reinforcement Learning New Study

Discover groundbreaking insights into the true effects of Reinforcement Learning with Verifiable Rewards (RLVR) on the reasoning capabilities of large language models (LLMs). In this AI Network News segment, Jai Patel breaks down the latest study from Tsinghua University and Shanghai Jiao Tong University that challenges long-held assumptions about reinforcement learning and reasoning capacity in AI models.

📊 Are RL-trained models really “smarter”?
Do they generate new reasoning abilities—or just sample more efficiently?

This paper investigates models like Qwen-2.5, LLaMA-3.1, and DeepSeek-R1 across tasks in math, code generation, and visual reasoning. Surprisingly, the study reveals that RLVR doesn't create new reasoning paths—it just boosts chances of hitting a correct answer early… while limiting exploration.

🧠 Authors: Yang Yue, Zhiqi Chen, Rui Lu, Andrew Zhao, Zhaokai Wang, Shiji Song, Gao Huang
🏫 Institutions: LeapLab, Tsinghua University; Shanghai Jiao Tong University
📄 Read the original research: https://arxiv.org/abs/2504.13837

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