1 citations · 1 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
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…