5 papers · 1 filter
Towards Efficient and Expressive Offline RL via Flow-Anchored Noise-conditioned Q-Learning
Sungyoung Lee, Dohyeong Kim, Eshan Balachandar +2
We propose Flow-Anchored Noise-conditioned Q-Learning (FAN), a highly efficient and high-performing offline reinforcement learning (RL) algorithm. Recent work has shown that expres…
Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants
Bozhi You, Irene Wang, Zelal Su Mustafaoglu +5
Attention is a fundamental building block of large language models (LLMs), so there have been many efforts to implement it efficiently. For example, FlashAttention leverages tiling…
Optimize Wider, Not Deeper: Consensus Aggregation for Policy Optimization
Zelal Su, Mustafaoglu, Sungyoung Lee +3
Proximal policy optimization (PPO) approximates the trust region update using multiple epochs of clipped SGD. Each epoch may drift further from the natural gradient direction, crea…
Evolutionary Policy Optimization
Zelal Su "Lain" Mustafaoglu, Keshav Pingali, Risto Miikkulainen
A key challenge in reinforcement learning (RL) is managing the exploration-exploitation trade-off without sacrificing sample efficiency. Policy gradient (PG) methods excel in explo…
HyperQuery: Beyond Binary Link Prediction
Sepideh Maleki, Josh Vekhter, Keshav Pingali
Groups with complex set intersection relations are a natural way to model a wide array of data, from the formation of social groups to the complex protein interactions which form t…