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References (35)

[1]
Group Sequence Policy Optimization
2025Chujie Zheng, Shixuan Liu et al.
[2]
RLPR: Extrapolating RLVR to General Domains without Verifiers
2025Tianyu Yu, Bo Ji et al.
[3]
Reasoning with Exploration: An Entropy Perspective
2025Daixuan Cheng, Shaohan Huang et al.
[4]
MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
2025MiniMax Aili Chen, Aonian Li et al.
[5]
The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models
2025Ganqu Cui, Yuchen Zhang et al.
[6]
Reinforcing General Reasoning without Verifiers
2025Xiangxin Zhou, Zi-Yan Liu et al.
[7]
TTRL: Test-Time Reinforcement Learning
2025Yuxin Zuo, Kaiyan Zhang et al.
[8]
Understanding R1-Zero-Like Training: A Critical Perspective
2025Zi-Yan Liu, Changyu Chen et al.
[9]
Beyond Verifiable Rewards: Scaling Reinforcement Learning for Language Models to Unverifiable Data
2025Yunhao Tang, Sid Wang et al.
[10]
Direct Reasoning Optimization: LLMs Can Reward And Refine Their Own Reasoning for Open-Ended Tasks
2025Yifei Xu, Tusher Chakraborty et al.
[11]
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
2025Adam Suma, Samuel Dauncey
[12]
Deliberative Alignment: Reasoning Enables Safer Language Models
2024Melody Y. Guan, Manas R. Joglekar et al.
[13]
TÜLU 3: Pushing Frontiers in Open Language Model Post-Training
2024Nathan Lambert, Jacob Daniel Morrison et al.
[14]
Language Models are Hidden Reasoners: Unlocking Latent Reasoning Capabilities via Self-Rewarding
2024Haolin Chen, Yihao Feng et al.
[15]
HybridFlow: A Flexible and Efficient RLHF Framework
2024Guangming Sheng, Chi Zhang et al.
[16]
The Llama 3 Herd of Models
2024Abhimanyu Dubey, Abhinav Jauhri et al.
[17]
OLMES: A Standard for Language Model Evaluations
2024Yuling Gu, Oyvind Tafjord et al.
[18]
General Purpose Verification for Chain of Thought Prompting
2024Robert Vacareanu, Anurag Pratik et al.
[19]
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
2024Zhihong Shao, Peiyi Wang et al.
[20]
Self-Rewarding Language Models
2024Weizhe Yuan, Richard Yuanzhe Pang et al.

Showing 20 of 35 references

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"NRT offers a novel framework for enhancing AI reasoning abilities without relying on costly, expert-verified data."

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2.5

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