Search Results for author:
Found 9 papers, 6 papers with code
Teaching Large Language Models to Reason with Reinforcement Learning
Surprisingly, we find the sample complexity of Expert Iteration is similar to that of PPO, requiring at most on the order of $10^6$ samples to converge from a pretrained checkpoint.
Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts
As large language models (LLMs) become increasingly prevalent across many real-world applications, understanding and enhancing their robustness to user inputs is of paramount importance.
Generalization to New Sequential Decision Making Tasks with In-Context Learning
By training on large diverse offline datasets, our model is able to learn new MiniHack and Procgen tasks without any weight updates from just a handful of demonstrations.
Multi-Objective GFlowNets
We study the problem of generating diverse candidates in the context of Multi-Objective Optimization.
Continual Learning In Environments With Polynomial Mixing Times
The mixing time of the Markov chain induced by a policy limits performance in real-world continual learning scenarios.
Compositional Attention: Disentangling Search and Retrieval
Through our qualitative analysis, we demonstrate that Compositional Attention leads to dynamic specialization based on the type of retrieval needed.
Curriculum in Gradient-Based Meta-Reinforcement Learning
However, specifically in the case of meta-reinforcement learning (meta-RL), we can show that gradient-based meta-learners are sensitive to task distributions.
Generating Automatic Curricula via Self-Supervised Active Domain Randomization
Goal-directed Reinforcement Learning (RL) traditionally considers an agent interacting with an environment, prescribing a real-valued reward to an agent proportional to the completion of some goal.
Data Efficient Stagewise Knowledge Distillation
We show, across the tested tasks, significant performance gains even with a fraction of the data used in distillation, without compromising on the metric.
