Hugging Face Trending Papers July 6, 2026

Weak-to-Strong Generalization via Direct On-Policy Distillation

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Reinforcement learning with verifiable rewards (RLVR) is a powerful recipe for improving language-model reasoning, but it is expensive to repeat on every new strong model because the target model must generate many rollouts during training. As models scale, post-training itself becomes a bottleneck.

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arXiv AI

Jul 7

Weak-to-Strong Generalization via Direct On-Policy Distillation

arXiv:2607. 05394v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is a powerful recipe for improving language-model reasoning, but it is expensive to repeat on every new strong model because the target model must generate many rollouts during training.

By Shiyuan Feng, Huan-ang Gao, Haohan Chi, Hanlin Wu, Zhilong Zhang, Zheng Jiang, Bingxiang He, Wei-Ying Ma, Ya-Qin Zhang, Hao Zhou

reinforcement-learning efficiency

arXiv Machine Learning

Jul 22

H$^2$SD: Hybrid Hindsight Self-Distillation

arXiv:2607. 18955v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has substantially improved the reasoning capabilities of large language models on tasks such as mathematical reasoning and code generation.

By Qiye Cai, Yichuan Ma, Linyang Li, Peiji Li, Yongkang Chen, Qipeng Guo, Yicheng Zou, Tao Gui, Xiaocheng Feng, Bing Qin

llms reinforcement-learning efficiency benchmarks

arXiv Machine Learning

Jul 30

Weak-to-Strong On-Policy Distillation

arXiv:2607. 26246v1 Announce Type: new Abstract: On-policy distillation (OPD), which aligns a student with the teacher's token-level distribution on the student's own rollouts, is an effective paradigm for transferring capabilities across LLMs.

By Fangxu Yu, Zinan Lin, Xiaodong Liu, Weijia Xu, Michael Xu, Tianyi Zhou, Jianfeng Gao

llms reinforcement-learning efficiency benchmarks

arXiv AI

Jul 23

REGEN: Replay-recycling for Expert-to-Generalist distillation with Offline Reinforcement Learning

arXiv:2607. 19450v1 Announce Type: cross Abstract: Large-scale online reinforcement learning (RL) is the predominant means of eliciting advanced abilities including long-term reasoning and agentic tool use in large language models (LLMs).

By Yunjie Chen, Xiaoxin Chen, Fang Wang

llms agents reinforcement-learning efficiency

arXiv Machine Learning

Jul 31

$\beta$-OPSD: Deriving with Policy Optimization, Training with Self-Distillation

arXiv:2607. 28582v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) is a promising approach to improve reasoning language models, but it remains brittle in practice: making it work reliably often requires substantial engineering effort.

By Jiawei Xu, Minghui Liu, Juzheng Zhang, Tom Goldstein, Furong Huang

llms reinforcement-learning efficiency benchmarks

arXiv AI

Jul 21

Distilled Reinforcement Learning for LLM Post-training

arXiv:2607. 17247v1 Announce Type: cross Abstract: Large language model (LLM) post-training is essential for improving reasoning, adaptation, and alignment.

By Chen Wang, Zhaochun Li, Jionghao Bai, Yining Zhang, Hexuan Deng, Ge Lan, Yue Wang

llms reinforcement-learning efficiency safety

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Weak-to-Strong Generalization via Direct On-Policy Distillation

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Weak-to-Strong On-Policy Distillation

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