5 papers
Meta-RL Induces Exploration in Language Agents
Yulun Jiang, Liangze Jiang, Damien Teney +2
Reinforcement learning (RL) has enabled the training of large language model (LLM) agents to interact with the environment and to solve multi-turn long-horizon tasks. However, the…
Can You Learn to See Without Images? Procedural Warm-Up for Vision Transformers
Zachary Shinnick, Liangze Jiang, Hemanth Saratchandran +2
Transformers are remarkably versatile, suggesting the existence of generic inductive biases beneficial across modalities. In this work, we explore a new way to instil such biases i…
Transformers Pretrained on Procedural Data Contain Modular Structures for Algorithmic Reasoning
Zachary Shinnick, Liangze Jiang, Hemanth Saratchandran +2
Pretraining on large, semantically rich datasets is key for developing language models. Surprisingly, recent studies have shown that even synthetic data, generated procedurally thr…
Do We Always Need the Simplicity Bias? Looking for Optimal Inductive Biases in the Wild
Damien Teney, Liangze Jiang, Florin Gogianu +1
Neural architectures tend to fit their data with relatively simple functions. This "simplicity bias" is widely regarded as key to their success. This paper explores the limits of t…
OOD-Chameleon: Is Algorithm Selection for OOD Generalization Learnable?
Liangze Jiang, Damien Teney
Out-of-distribution (OOD) generalization is challenging because distribution shifts come in many forms. Numerous algorithms exist to address specific settings, but choosing the rig…