1 citations · 2 across the 5 of their papers we have counts for
6 papers
Cortex: Consolidating Context into Parametric Memory
Jintian Zhang, Feng He, Xinnian Liang +5
Context can improve large language model performance, but these gains may be lost after context removal. Inspired by biological systems consolidation, we formulate Memory Consolida…
Looking beyond the next token
Abitha Thankaraj, Yiding Jiang, J. Zico Kolter +1
The structure of causal language model training assumes that each token can be accurately predicted from the previous context. This contrasts with humans' natural writing and reaso…
Training a Generally Curious Agent
Fahim Tajwar, Yiding Jiang, Abitha Thankaraj +4
Efficient exploration is essential for intelligent systems interacting with their environment, but existing language models often fall short in scenarios that require strategic inf…
That Sounds Right: Auditory Self-Supervision for Dynamic Robot Manipulation
Abitha Thankaraj, Lerrel Pinto
Learning to produce contact-rich, dynamic behaviors from raw sensory data has been a longstanding challenge in robotics. Prominent approaches primarily focus on using visual or tac…
Context is Everything: Implicit Identification for Dynamics Adaptation
Ben Evans, Abitha Thankaraj, Lerrel Pinto
Understanding environment dynamics is necessary for robots to act safely and optimally in the world. In realistic scenarios, dynamics are non-stationary and the causal variables su…
RB2: Robotic Manipulation Benchmarking with a Twist
Sudeep Dasari, Jianren Wang, Joyce Hong +12
Benchmarks offer a scientific way to compare algorithms using objective performance metrics. Good benchmarks have two features: (a) they should be widely useful for many research g…