15 citations · 16 across the 15 of their papers we have counts for
4 papers · 1 filter
In-Context Learning Strategies Emerge Rationally
Daniel Wurgaft, Ekdeep Singh Lubana, Core Francisco Park +3
Recent work analyzing in-context learning (ICL) has identified a broad set of strategies that describe model behavior in different experimental conditions. We aim to unify these fi…
Decomposing Elements of Problem Solving: What "Math" Does RL Teach?
Tian Qin, Core Francisco Park, Mujin Kwun +5
Mathematical reasoning tasks have become prominent benchmarks for assessing the reasoning capabilities of LLMs, especially with reinforcement learning (RL) methods such as GRPO sho…
: System-2 Fine-tuning for Robust Integration of New Knowledge
Core Francisco Park, Zechen Zhang, Hidenori Tanaka
Humans and intelligent animals can internalize new information and accurately internalize their implications to perform downstream tasks. While large language models (LLMs) can ach…
ICLR: In-Context Learning of Representations
Core Francisco Park, Andrew Lee, Ekdeep Singh Lubana +5
Recent work has demonstrated that semantics specified by pretraining data influence how representations of different concepts are organized in a large language model (LLM). However…