Tootfinder

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs
Yifan Zhao, Egan Johnson, Prasanth Chatarasi, Vikram Adve, Sasa Misailovic
https://arxiv.org/abs/2510.08726

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs
Operator fusion has become a key optimization for deep learning, which combines multiple deep learning operators to improve data reuse and reduce global memory transfers. However, existing tensor compilers struggle to fuse complex reduction computations involving loop-carried dependencies, such as attention mechanisms. The paper introduces Neptune, a tensor compiler for advanced operator fusion for sequences of reduction operators. Neptune presents a new approach for advanced operator fusion,…

Assessing Policy Updates: Toward Trust-Preserving Intelligent User Interfaces
Matan Solomon, Ofra Amir, Omer Ben-Porat
https://arxiv.org/abs/2510.10616 https://

Assessing Policy Updates: Toward Trust-Preserving Intelligent User Interfaces
Reinforcement learning agents are often updated with human feedback, yet such updates can be unreliable: reward misspecification, preference conflicts, or limited data may leave policies unchanged or even worse. Because policies are difficult to interpret directly, users face the challenge of deciding whether an update has truly helped. We propose that assessing model updates -- not just a single model -- is a critical design challenge for intelligent user interfaces. In a controlled study, par…

Agile in the Face of Delay: Asynchronous End-to-End Learning for Real-World Aerial Navigation
Yude Li, Zhexuan Zhou, Huizhe Li, Youmin Gong, Jie Mei
https://arxiv.org/abs/2509.13816

Agile in the Face of Delay: Asynchronous End-to-End Learning for Real-World Aerial Navigation
Robust autonomous navigation for Autonomous Aerial Vehicles (AAVs) in complex environments is a critical capability. However, modern end-to-end navigation faces a key challenge: the high-frequency control loop needed for agile flight conflicts with low-frequency perception streams, which are limited by sensor update rates and significant computational cost. This mismatch forces conventional synchronous models into undesirably low control rates. To resolve this, we propose an asynchronous reinfo…

Dual-Actor Fine-Tuning of VLA Models: A Talk-and-Tweak Human-in-the-Loop Approach
Piaopiao Jin, Qi Wang, Guokang Sun, Ziwen Cai, Pinjia He, Yangwei You
https://arxiv.org/abs/2509.13774

Dual-Actor Fine-Tuning of VLA Models: A Talk-and-Tweak Human-in-the-Loop Approach
Vision-language-action (VLA) models demonstrate strong generalization in robotic manipulation but face challenges in complex, real-world tasks. While supervised fine-tuning with demonstrations is constrained by data quality, reinforcement learning (RL) offers a promising alternative. We propose a human-in-the-loop dual-actor fine-tuning framework grounded in RL. The framework integrates a primary actor for robust multi-task performance with a refinement actor for latent-space adaptation. Beyond…