291 citations · 957 across the 23 of their papers we have counts for
12 papers · 1 filter
NeuroLens: Learning Latent Embeddings of Neural Semantics from Chronic Recordings
Hanrui Lyu, Baiyuan Chen, Tianshu Tan +8
Understanding how neural activity represents higher-order cognition and how these representations evolve over time has long been a central pursuit in neuroscience. However, current…
Temporal Leakage in LLM Backtesting: Measurement, Validation, and Adjusted Scores
Zeyu Zhang, Bradly C. Stadie
The standard check for contamination in LLM backtests is simple: compare scores before and after the training cutoff. We show this check is uninformative. Four flagship models fail…
TEMPO: Temporal Enforcement via Mode-Separated Policy Optimization for Trustworthy LLM Backtesting
Zeyu Zhang, Bradly C. Stadie
Backtesting large language models on historical events requires reasoning exclusively from information available before a specified cutoff date. Yet models routinely leak post-cuto…
D2 Actor Critic: Diffusion Actor Meets Distributional Critic
Lunjun Zhang, Shuo Han, Hanrui Lyu +1
We introduce D2AC, a new model-free reinforcement learning (RL) algorithm designed to train expressive diffusion policies online effectively. At its core is a policy improvement ob…
Of Mice and Machines: A Comparison of Learning Between Real World Mice and RL Agents
Shuo Han, German Espinosa, Junda Huang +3
Recent advances in reinforcement learning (RL) have demonstrated impressive capabilities in complex decision-making tasks. This progress raises a natural question: how do these art…
LAMP: Extracting Local Decision Surfaces From Large Language Models
Ryan Chen, Youngmin Ko, Zeyu Zhang +5
We introduce LAMP (Local Attribution Mapping Probe), a method that shines light onto a black-box language model's decision surface and studies how reliably a model maps its stated…
Understanding Hindsight Goal Relabeling from a Divergence Minimization Perspective
Lunjun Zhang, Bradly C. Stadie
Hindsight goal relabeling has become a foundational technique in multi-goal reinforcement learning (RL). The essential idea is that any trajectory can be seen as a sub-optimal demo…
Invariance Through Latent Alignment
Takuma Yoneda, Ge Yang, Matthew R. Walter +1
A robot's deployment environment often involves perceptual changes that differ from what it has experienced during training. Standard practices such as data augmentation attempt to…
Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning
Silviu Pitis, Harris Chan, Stephen Zhao +2
What goals should a multi-goal reinforcement learning agent pursue during training in long-horizon tasks? When the desired (test time) goal distribution is too distant to offer a u…
One-Shot Pruning of Recurrent Neural Networks by Jacobian Spectrum Evaluation
Matthew Shunshi Zhang, Bradly Stadie
Recent advances in the sparse neural network literature have made it possible to prune many large feed forward and convolutional networks with only a small quantity of data. Yet, t…
Evolved Policy Gradients
Rein Houthooft, Richard Y. Chen, Phillip Isola +4
We propose a metalearning approach for learning gradient-based reinforcement learning (RL) algorithms. The idea is to evolve a differentiable loss function, such that an agent, whi…
Third-Person Imitation Learning
Bradly C. Stadie, Pieter Abbeel, Ilya Sutskever
Reinforcement learning (RL) makes it possible to train agents capable of achieving sophisticated goals in complex and uncertain environments. A key difficulty in reinforcement lear…