2 citations · 4 across the 4 of their papers we have counts for
6 papers · 1 filter
Strategy Coopetition Explains the Emergence and Transience of In-Context Learning
Aaditya K. Singh, Ted Moskovitz, Sara Dragutinovic +3
In-context learning (ICL) is a powerful ability that emerges in transformer models, enabling them to learn from context without weight updates. Recent work has established emergent…
HARP: A challenging human-annotated math reasoning benchmark
Albert S. Yue, Lovish Madaan, Ted Moskovitz +2
Math reasoning is becoming an ever increasing area of focus as we scale large language models. However, even the previously-toughest evals like MATH are now close to saturated by f…
What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation
Aaditya K. Singh, Ted Moskovitz, Felix Hill +2
In-context learning is a powerful emergent ability in transformer models. Prior work in mechanistic interpretability has identified a circuit element that may be critical for in-co…
The Transient Nature of Emergent In-Context Learning in Transformers
Aaditya K. Singh, Stephanie C. Y. Chan, Ted Moskovitz +3
Transformer neural networks can exhibit a surprising capacity for in-context learning (ICL) despite not being explicitly trained for it. Prior work has provided a deeper understand…
Confronting Reward Model Overoptimization with Constrained RLHF
Ted Moskovitz, Aaditya K. Singh, DJ Strouse +4
Large language models are typically aligned with human preferences by optimizing (RMs) fitted to human feedback. However, human preferences are multi-facet…
A State Representation for Diminishing Rewards
Ted Moskovitz, Samo Hromadka, Ahmed Touati +2
A common setting in multitask reinforcement learning (RL) demands that an agent rapidly adapt to various stationary reward functions randomly sampled from a fixed distribution. In…