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References (50)

[1]
Nemotron-Cascade: Scaling Cascaded Reinforcement Learning for General-Purpose Reasoning Models
2025Boxin Wang, Chankyu Lee et al.
[2]
CurES: From Gradient Analysis to Efficient Curriculum Learning for Reasoning LLMs
2025Yongcheng Zeng, Zexu Sun et al.
[3]
FlowRL: Matching Reward Distributions for LLM Reasoning
2025Xuekai Zhu, Daixuan Cheng et al.
[4]
Jointly Reinforcing Diversity and Quality in Language Model Generations
2025Tianjian Li, Yiming Zhang et al.
[5]
Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models
2025Zhipeng Chen, Xiaobo Qin et al.
[6]
Can Prompt Difficulty be Online Predicted for Accelerating RL Finetuning of Reasoning Models?
2025Yun Qu, Qi Cheems Wang et al.
[7]
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
2025Gheorghe Comanici, Eric Bieber et al.
[8]
EFRame: Deeper Reasoning via Exploration-Filter-Replay Reinforcement Learning Framework
2025Chen Wang, Lai Wei et al.
[9]
RePO: Replay-Enhanced Policy Optimization
2025Siheng Li, Zhanhui Zhou et al.
[10]
Act Only When It Pays: Efficient Reinforcement Learning for LLM Reasoning via Selective Rollouts
2025Haizhong Zheng, Yang Zhou et al.
[11]
The Surprising Effectiveness of Negative Reinforcement in LLM Reasoning
2025Xinyu Zhu, Mengzhou Xia et al.
[12]
Learning Like Humans: Advancing LLM Reasoning Capabilities via Adaptive Difficulty Curriculum Learning and Expert-Guided Self-Reformulation
2025Enci Zhang, Xingang Yan et al.
[13]
Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?
2025Yang Yue, Zhiqi Chen et al.
[14]
Evaluating the Diversity and Quality of LLM Generated Content
2025Alexander Shypula, Shuo Li et al.
[15]
Efficient Reinforcement Finetuning via Adaptive Curriculum Learning
2025Taiwei Shi, Yiyang Wu et al.
[16]
Online Difficulty Filtering for Reasoning Oriented Reinforcement Learning
2025Sanghwan Bae, Jiwoo Hong et al.
[17]
Trajectory Balance with Asynchrony: Decoupling Exploration and Learning for Fast, Scalable LLM Post-Training
2025Brian R. Bartoldson, S. Venkatraman et al.
[18]
DAPO: An Open-Source LLM Reinforcement Learning System at Scale
2025Qiying Yu, Zheng Zhang et al.
[19]
Learning to Reason at the Frontier of Learnability
2025Thomas Foster, Jakob Foerster
[20]
Self-Improvement in Language Models: The Sharpening Mechanism
2024Audrey Huang, Adam Block et al.

Showing 20 of 50 references

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