arXiv Machine Learning June 9, 2026 By Ke Wang, Shuangqi Li, Mathieu Salzmann, Pascal Frossard

PriFT: Prior-Support Guided Supervised Fine-Tuning

Read the original on arXiv Machine Learning →

arXiv:2606. 09396v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) is an efficient approach for downstream task adaptation and often serves as the initialization stage for reinforcement learning (RL), but it can show weaker generalization than RL.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Jun 10

When RL Fails after SFT: Rejuvenating Model Plasticity for Robust SFT-to-RL Handoff

arXiv:2606. 09932v1 Announce Type: cross Abstract: Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) has become a standard pipeline for Large Language Model (LLM) post-training.

By Runze Liu, Jiashun Liu, Xu Wan, Yuqian Fu, Ling Pan

llms agents reinforcement-learning fine-tuning

arXiv AI

Aug 11

Proxy OPD: On-Policy Distillation with Transferable Relative Proxy Update

arXiv:2607. 11505v2 Announce Type: replace-cross Abstract: Post-training for large language models typically couples policy exploration with model optimization, hindering the reuse of high-reward behaviors from policy exploration.

By Daocheng Fu, Rong Wu, Yu Yang, Jianbiao Mei, Licheng Wen, Pinlong Cai, Xuemeng Yang, Yong Liu, Botian Shi, Yu Qiao

llms efficiency

arXiv AI

Jun 10

A Unifying Lens on Supervised Fine-Tuning Through Target Distribution Design

arXiv:2606. 11189v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) typically maximizes the likelihood of every token in a demonstrated trajectory.

By Tong Xie, Yuanhao Ban, Yunqi Hong, Sohyun An, Yihang Chen, Cho-Jui Hsieh

fine-tuning

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

2d ago

Finetuning with Sampling: SFT Learns Better Than You Think

arXiv:2610.02140v1 Announce Type: cross Abstract: Introducing new capabilities to frontier models has long been the goal of posttraining, which predominantly employs supervised finetuning (SFT) and r...

By Aayush Karan, Sitan Chen, Yilun Du

reinforcement-learning fine-tuning

arXiv Machine Learning

Jul 7

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure

arXiv:2607. 04733v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) is the standard approach for adapting pretrained language models to downstream domains, yet it often improves target-domain behavior at the cost of degrading pre-existing capabilities.

By Yueyang Wang, Baolong Bi, Shuo Lu, Jingyuan Zhang

llms fine-tuning multimodal

One email a morning, machine-written

Related stories

When RL Fails after SFT: Rejuvenating Model Plasticity for Robust SFT-to-RL Handoff

Proxy OPD: On-Policy Distillation with Transferable Relative Proxy Update

A Unifying Lens on Supervised Fine-Tuning Through Target Distribution Design

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

Finetuning with Sampling: SFT Learns Better Than You Think

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure