1 citations · 1 across the 7 of their papers we have counts for
8 papers
Inference-Time Distillation: Cost-Efficient Agents Without Fine-Tuning or Manual Prompt Engineering
Vishnu Sarukkai, Asanshay Gupta, James Hong +2
Deploying LLM agents at scale typically requires choosing between quality and cost. Existing cost-reduction approaches fail to preserve agility: the ability to iterate rapidly with…
Learning to Ball: Composing Policies for Long-Horizon Basketball Moves
Pei Xu, Zhen Wu, Ruocheng Wang +5
Learning a control policy for a multi-phase, long-horizon task, such as basketball maneuvers, remains challenging for reinforcement learning approaches due to the need for seamless…
Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks
Vishnu Sarukkai, Zhiqiang Xie, Kayvon Fatahalian
Improving Large Language Model (LLM) agents for sequential decision-making tasks typically requires extensive task-specific knowledge engineering--custom prompts, curated examples,…
Automated Rewards via LLM-Generated Progress Functions
Vishnu Sarukkai, Brennan Shacklett, Zander Majercik +3
Large Language Models (LLMs) have the potential to automate reward engineering by leveraging their broad domain knowledge across various tasks. However, they often need many iterat…
Learning to Move Like Professional Counter-Strike Players
David Durst, Feng Xie, Vishnu Sarukkai +9
In multiplayer, first-person shooter games like Counter-Strike: Global Offensive (CS:GO), coordinated movement is a critical component of high-level strategic play. However, the co…
Block and Detail: Scaffolding Sketch-to-Image Generation
Vishnu Sarukkai, Lu Yuan, Mia Tang +2
We introduce a novel sketch-to-image tool that aligns with the iterative refinement process of artists. Our tool lets users sketch blocking strokes to coarsely represent the placem…
Low-Shot Validation: Active Importance Sampling for Estimating Classifier Performance on Rare Categories
Fait Poms, Vishnu Sarukkai, Ravi Teja Mullapudi +4
For machine learning models trained with limited labeled training data, validation stands to become the main bottleneck to reducing overall annotation costs. We propose a statistic…
Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks
Vishnu Sarukkai, Anirudh Jain, Burak Uzkent +1
Satellite images hold great promise for continuous environmental monitoring and earth observation. Occlusions cast by clouds, however, can severely limit coverage, making ground in…