arXiv AI October 15, 2026 By Huihao Jing, Wenbin Hu, Shaojin Chen, Haochen Shi, Zhongwei Xie, Guijia Zhang, Yuxuan Liu, Haoyu Huang, Haoran Li, Yangqiu Song

SoFT: Soft Targets for Generalizable LLM Fine-Tuning

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arXiv Machine Learning

Jun 11

Compatibility-Aware Dynamic Fine-Tuning for Large Language Models

arXiv:2606. 11206v1 Announce Type: cross Abstract: Supervised Fine-Tuning (SFT) is the predominant paradigm for aligning large language models (LLMs), yet it suffers from optimization instability and limited generalization.

By Yucheng Zhou, Junwei Sheng, Qianning Wang, Jianbing Shen

llms reinforcement-learning fine-tuning

arXiv Machine Learning

6d ago

RM-OPD: On-Policy Distillation for Generative Reward Models

arXiv:2610.20174v1 Announce Type: new Abstract: Reinforcement Learning on Mixed-domain preference data (Mix-RL) has become the mainstream approach for training general-purpose Generative Reward Model...

By Qinglin Su, Licheng Pan, Zhen Fang, Jiakang Wang, Qingnan Ren, Yunsheng Lu, Yan Gao, Zhixuan Chu

reinforcement-learning fine-tuning efficiency benchmarks

arXiv AI

Jul 14

Stable On-Policy Distillation through Adaptive Target Reformulation

arXiv:2601. 07155v3 Announce Type: replace-cross Abstract: Knowledge distillation (KD) is a widely adopted technique for transferring knowledge from large language models to smaller student models; however, conventional supervised KD often suffers from a distribution mismatch between training and inference.

By Ijun Jang, Jewon Yeom, Juan Yeo, Hyunggyu Lim, Taesup Kim

llms fine-tuning efficiency safety

arXiv AI

Jun 19

Reinforcement-aware Knowledge Distillation for LLM Reasoning

arXiv:2602. 22495v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) post-training has recently driven major gains in long chain-of-thought reasoning large language models (LLMs), but the high inference cost of such models motivates distillation into smaller students.

By Zhaoyang Zhang, Shuli Jiang, Yantao Shen, Yuting Zhang, Dhananjay Ram, Shuo Yang, Zhuowen Tu, Wei Xia, Stefano Soatto

llms reinforcement-learning fine-tuning efficiency benchmarks

arXiv Computation and Language

Aug 28

Consolidating RLVR Capabilities Across Domains: A Deep Dive into Fusion Paradigms

The paper investigates three fusion paradigms—Merge, Mix RL, and multi‑teacher on‑policy distillation (MOPD)—for consolidating reinforcement learning with verifiable rewards (RLVR) across multiple domains. Experiments across model scales and a multi‑domain benchmark show that while overall performance differences are small, significant gaps can appear on specific tasks, and each method exhibits distinct training dynamics and constraints. Practical guidelines are offered: Merge for cheap fusion when experts exist, Mix RL for unified training with adjustable domain mixtures, and MOPD when preserving domain‑specific gains is paramount.

By Siye Wu, Kai Yang, Yuchen Cai, Xin Xu, Peng-Yuan Wang, Jiaxuan Wang, Jiashun Liu, Jiafei Lyu, Yangkun Chen, Saiyong Yang, Yanghua Xiao

llms reinforcement-learning efficiency benchmarks

Hugging Face Trending Papers

Aug 17

Every Coin Has Two Sides: On the Dual Nature of Generalization in On-Policy Distillation of Large Language Models

On-policy distillation (OPD) transfers teacher capabilities by supervising trajectories sampled from the student's own policy, yet its generalization behavior remains poorly understood, as most studies evaluate OPD on a single domain and on benchmarks close to the training data. We present a controlled study that varies one generalization factor at a time, from in-domain distribution shifts to cross-domain transfer and the multi-teacher setting.

llms efficiency benchmarks