arXiv AI July 29, 2026 By Cen Lu, Yung-Chen Tang, Andrea Cavallaro

Similar Models Learn Differently: Final-Window Pretraining Shapes Post-Training Beyond SFT

arXiv:2607. 25063v1 Announce Type: new Abstract: Developers judge a model checkpoint by how it behaves.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Aug 11

Early Data Exposure Improves Robustness to Subsequent Fine-Tuning

arXiv:2605. 12705v2 Announce Type: replace Abstract: How can we train models whose post-trained capabilities survive subsequent fine-tuning?

By Lawrence Feng, Gaurav R. Ghosal, Jacob Mitchell Springer, Ziqian Zhong, Aditi Raghunathan

fine-tuning

arXiv Machine Learning

Jul 30

Shared SFT Lessons Across Alignment, Model Organisms, and Toy Models

arXiv:2607. 26173v1 Announce Type: new Abstract: Alignment training, model organisms, and toy models are usually treated as separate research areas.

By Anton de la Fuente, Arthur Conmy

llms rag fine-tuning safety

arXiv Machine Learning

Jul 22

Mark, Don't Erase: Token Inoculation for Dual-Use Knowledge in LLMs

arXiv:2607. 18639v1 Announce Type: new Abstract: Safety interventions on dual-use knowledge typically choose between destroying hazardous content (e.

By Seunghyun Lee, Dongyoon Han, Sangdoo Yun

llms fine-tuning benchmarks safety

arXiv Machine Learning

Jul 30

Constitutional Midtraining: Content Presence Drives Alignment Gains

arXiv:2607. 26654v1 Announce Type: cross Abstract: Post-training alignment is often shallow, eroding under fine-tuning.

By Desiree Cho, Cameron Tice, Bernie Hogan, Hunar Batra, Puria Radmard, Jun Zhao, Nigel Shadbolt

fine-tuning benchmarks safety

arXiv AI

Jul 22

Measuring Reward-Seeking via Contrastive Belief Updates

arXiv:2607. 18966v1 Announce Type: new Abstract: Language models trained with reinforcement learning may learn to optimize the grader's judgment rather than the intended objective.

By Axel H{\o}jmark, J\'er\'emy Scheurer, Evgenia Nitishinskaya, Felix Hofst\"atter, Jason Wolfe, Theodore Ehrenborg, Bronson Schoen, Alexander Meinke

llms reinforcement-learning fine-tuning safety

arXiv AI

Jul 3

Procedural Memory Distillation: Online Reflection for Self-Improving Language Models

arXiv:2607. 01480v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR), along with recent selfdistillation variants such as SDPO, evaluates each rollout against a verifier and updates the policy from that episode-level signal.

By Ye Liu, Srijan Bansal, Bo Pang, Yang Li, Zeyu Leo Liu, Yifei Ming, Zixuan Ke, Shafiq Joty, Semih Yavuz

llms reinforcement-learning efficiency

Related stories

Early Data Exposure Improves Robustness to Subsequent Fine-Tuning

Shared SFT Lessons Across Alignment, Model Organisms, and Toy Models

Mark, Don't Erase: Token Inoculation for Dual-Use Knowledge in LLMs

Constitutional Midtraining: Content Presence Drives Alignment Gains

Measuring Reward-Seeking via Contrastive Belief Updates

Procedural Memory Distillation: Online Reflection for Self-Improving Language Models