4 papers
Scalable Training of Mixture-of-Experts Models with Megatron Core
Zijie Yan, Hongxiao Bai, Xin Yao +42
Scaling Mixture-of-Experts (MoE) training introduces systems challenges absent in dense models. Because each token activates only a subset of experts, this sparsity allows total pa…
Aligning LLMs with Domain Invariant Reward Models
David Wu, Sanjiban Choudhury
Aligning large language models (LLMs) to human preferences is challenging in domains where preference data is unavailable. We address the problem of learning reward models for such…
The Virtues of Pessimism in Inverse Reinforcement Learning
David Wu, Gokul Swamy, J. Andrew Bagnell +2
Inverse Reinforcement Learning (IRL) is a powerful framework for learning complex behaviors from expert demonstrations. However, it traditionally requires repeatedly solving a comp…
Accelerating Inverse Reinforcement Learning with Expert Bootstrapping
David Wu, Sanjiban Choudhury
Existing inverse reinforcement learning methods (e.g. MaxEntIRL, -IRL) search over candidate reward functions and solve a reinforcement learning problem in the inner loop. This…