Hugging Face Trending Papers August 18, 2026

SignalReasoner: Assessing the Upper Bound of 3B Models for Signal Mathematical Reasoning

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Post-training with supervised chain-of-thought fine-tuning and reinforcement learning from verifiable rewards has substantially improved the mathematical reasoning capabilities of large language models (LLMs). However, their application to signal processing problems remains relatively under-explored.

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arXiv AI

1d ago

SignalReasoner: Assessing the Upper Bound of 3B Models for Signal Mathematical Reasoning

arXiv:2608. 17301v1 Announce Type: new Abstract: Post-training with supervised chain-of-thought fine-tuning and reinforcement learning from verifiable rewards has substantially improved the mathematical reasoning capabilities of large language models (LLMs).

By Guozheng Sun

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 2

RLVR without Ineffective Samples: Group Prioritized Off-Policy Optimization for LLM Reasoning

arXiv:2606. 01281v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for enhancing the reasoning capabilities of large language models (LLMs).

By Yixiu Mao, Yun Qu, Qi Wang, Heming Zou, Xiangyang Ji

llms reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jul 14

Nested-ReFT: Efficient Reinforcement Learning for Large Language Model Fine-Tuning via Off-Policy Rollouts

arXiv:2508. 10123v3 Announce Type: replace-cross Abstract: Advanced reasoning in LLMs on challenging domains like mathematical reasoning can be tackled using verifiable rewards based reinforced fine-tuning (ReFT).

By Maxime Heuillet, Yufei Cui, Boxing Chen, Audrey Durand, Prasanna Parthasarathi

llms reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jul 21

It Takes 8 Tokens: Weak-to-Strong Off-Policy RL via Auxiliary Branches

arXiv:2607. 16205v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards has emerged as a standard approach for enhancing reasoning in large language models, which typically optimizes the policy by contrasting multiple self generated rollouts.

By Dayu Wang, Jiaye Yang, Weikang Li, Jiahui Liang, Liwei Qian, Xin Pei, Jizhou Huang

llms reinforcement-learning benchmarks

arXiv AI

Jun 9

Momentum for Reasoning: Dense Intrinsic Signals in Policy Optimization

arXiv:2606. 08815v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for eliciting long-chain reasoning in large language models.

By Hao Chen, Zhanming Shen, Liyao Li, Yanyu Chen, Xuhang Zhu, Xiaomeng Hu, Qi Zhang, Ru Peng, Xiaoyu Shen, Haobo Wang, Junbo Zhao

llms reinforcement-learning benchmarks

arXiv Machine Learning

Jun 4

Policy Improvement Reinforcement Learning

arXiv:2604. 00860v3 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become a central post-training paradigm for improving the reasoning capabilities of large language models.

By Huaiyang Wang, Xiaojie Li, Deqing Wang, Haoyi Zhou, Zixuan Huang, Yaodong Yang, Jianxin Li, Yikun Ban

llms reinforcement-learning benchmarks

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SignalReasoner: Assessing the Upper Bound of 3B Models for Signal Mathematical Reasoning

RLVR without Ineffective Samples: Group Prioritized Off-Policy Optimization for LLM Reasoning

Nested-ReFT: Efficient Reinforcement Learning for Large Language Model Fine-Tuning via Off-Policy Rollouts

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Momentum for Reasoning: Dense Intrinsic Signals in Policy Optimization

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