3 citations · 9 across the 18 of their papers we have counts for
4 papers · 1 filter
Adaptation of Agentic AI: A Survey of Post-Training, Memory, and Skills
Pengcheng Jiang, Jiacheng Lin, Zhiyi Shi +31
Large language model (LLM) agents are moving beyond prompting alone. ChatGPT marked the rise of general-purpose LLM assistants, DeepSeek showed that on-policy reinforcement learnin…
Building A Proof-Oriented Programmer That Is 64% Better Than GPT-4o Under Data Scarcity
Dylan Zhang, Justin Wang, Tianran Sun
Existing LMs struggle with proof-oriented programming due to data scarcity, which manifest in two key ways: (1) a lack of sufficient corpora for proof-oriented programming language…
The Best Instruction-Tuning Data are Those That Fit
Dylan Zhang, Qirun Dai, Hao Peng
High-quality supervised fine-tuning (SFT) data are crucial for eliciting strong capabilities from pretrained large language models (LLMs). Typically, instructions are paired with m…
Improving Influence-based Instruction Tuning Data Selection for Balanced Learning of Diverse Capabilities
Qirun Dai, Dylan Zhang, Jiaqi W. Ma +1
Selecting appropriate training data is crucial for instruction fine-tuning of large language models (LLMs), which aims to (1) elicit strong capabilities, and (2) achieve balanced p…