Search Results for author:
Found 12 papers, 8 papers with code
What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation
By clamping subsets of activations throughout training, we then identify three underlying subcircuits that interact to drive IH formation, yielding the phase change.
Scaling Instructable Agents Across Many Simulated Worlds
Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI.
The Transient Nature of Emergent In-Context Learning in Transformers
The transient nature of ICL is observed in transformers across a range of model sizes and datasets, raising the question of how much to "overtrain" transformers when seeking compact, cheaper-to-run models.
Transformers generalize differently from information stored in context vs in weights
In transformers trained on controlled stimuli, we find that generalization from weights is more rule-based whereas generalization from context is largely exemplar-based.
Language models show human-like content effects on reasoning tasks
We evaluate state of the art large language models, as well as humans, and find that the language models reflect many of the same patterns observed in humans across these tasks $\unicode{x2014}$ like humans, models answer more accurately when the semantic content of a task supports the logical inferences.
Data Distributional Properties Drive Emergent In-Context Learning in Transformers
In further experiments, we found that naturalistic data distributions were only able to elicit in-context learning in transformers, and not in recurrent models.
Semantic Exploration from Language Abstractions and Pretrained Representations
We show that these pretrained representations drive meaningful, task-relevant exploration and improve performance on 3D simulated environments.
Can language models learn from explanations in context?
In summary, explanations can support the in-context learning of large LMs on challenging tasks.
Zipfian environments for Reinforcement Learning
As humans and animals learn in the natural world, they encounter distributions of entities, situations and events that are far from uniform.
Tell me why! Explanations support learning relational and causal structure
Inferring the abstract relational and causal structure of the world is a major challenge for reinforcement-learning (RL) agents.
Towards mental time travel: a hierarchical memory for reinforcement learning agents
Agents with common memory architectures struggle to recall and integrate across multiple timesteps of a past event, or even to recall the details of a single timestep that is followed by distractor tasks.
Measuring the Reliability of Reinforcement Learning Algorithms
To aid RL researchers and production users with the evaluation and improvement of reliability, we propose a set of metrics that quantitatively measure different aspects of reliability.
