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References (38)

[1]
Retaining by Doing: The Role of On-Policy Data in Mitigating Forgetting
2025Howard Chen, Noam Razin et al.
[2]
Understanding the Effects of Domain Finetuning on LLMs
2025Eshaan Tanwar, Deepak Nathani et al.
[3]
LoRA Without Regret
2025John Schulman
[4]
SFT Doesn't Always Hurt General Capabilities: Revisiting Domain-Specific Fine-Tuning in LLMs
2025Jiacheng Lin, Zhongruo Wang et al.
[5]
Small Batch Size Training for Language Models: When Vanilla SGD Works, and Why Gradient Accumulation Is Wasteful
2025Martin Marek, Sanae Lotfi et al.
[6]
Qwen3 Technical Report
2025An Yang, Anfeng Li et al.
[7]
Memorization vs. Reasoning: Updating LLMs with New Knowledge
2025Aochong Oliver Li, Tanya Goyal
[8]
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
2025Adam Suma, Samuel Dauncey
[9]
The Llama 3 Herd of Models
2024Abhimanyu Dubey, Abhinav Jauhri et al.
[10]
Domain Adaptation of Llama3-70B-Instruct through Continual Pre-Training and Model Merging: A Comprehensive Evaluation
2024Shamane Siriwardhana, Mark McQuade et al.
[11]
MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark
2024Yubo Wang, Xueguang Ma et al.
[12]
Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning
2024Zhaorui Yang, Qian Liu et al.
[13]
Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue
2024Jia-Chen Gu, Haoyang Xu et al.
[14]
GPQA: A Graduate-Level Google-Proof Q&A Benchmark
2023David Rein, Betty Li Hou et al.
[15]
Instruction-Following Evaluation for Large Language Models
2023Jeffrey Zhou, Tianjian Lu et al.
[16]
MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning
2023Zayne Sprague, Xi Ye et al.
[17]
Propagating Knowledge Updates to LMs Through Distillation
2023Shankar Padmanabhan, Yasumasa Onoe et al.
[18]
BioASQ-QA: A manually curated corpus for Biomedical Question Answering
2022Anastasia Krithara, A. Nentidis et al.
[19]
Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them
2022Mirac Suzgun, Nathan Scales et al.
[20]
Mass-Editing Memory in a Transformer
2022Kevin Meng, Arnab Sen Sharma et al.

Showing 20 of 38 references

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