3 citations · 4 across the 9 of their papers we have counts for
6 papers · 1 filter
Streetwise Agents: Empowering Offline RL Policies to Outsmart Exogenous Stochastic Disturbances in RTC
Aditya Soni, Mayukh Das, Anjaly Parayil +8
The difficulty of exploring and training online on real production systems limits the scope of real-time online data/feedback-driven decision making. The most feasible approach is…
CARMO: Dynamic Criteria Generation for Context-Aware Reward Modelling
Taneesh Gupta, Shivam Shandilya, Xuchao Zhang +5
Reward modeling in large language models is susceptible to reward hacking, causing models to latch onto superficial features such as the tendency to generate lists or unnecessarily…
TACO-RL: Task Aware Prompt Compression Optimization with Reinforcement Learning
Shivam Shandilya, Menglin Xia, Supriyo Ghosh +4
The increasing prevalence of large language models (LLMs) such as GPT-4 in various applications has led to a surge in the size of prompts required for optimal performance, leading…
X-lifecycle Learning for Cloud Incident Management using LLMs
Drishti Goel, Fiza Husain, Aditya Singh +5
Incident management for large cloud services is a complex and tedious process and requires significant amount of manual efforts from on-call engineers (OCEs). OCEs typically levera…
Dependency Aware Incident Linking in Large Cloud Systems
Supriyo Ghosh, Karish Grover, Jimmy Wong +4
Despite significant reliability efforts, large-scale cloud services inevitably experience production incidents that can significantly impact service availability and customer's sat…
Automated Root Causing of Cloud Incidents using In-Context Learning with GPT-4
Xuchao Zhang, Supriyo Ghosh, Chetan Bansal +4
Root Cause Analysis (RCA) plays a pivotal role in the incident diagnosis process for cloud services, requiring on-call engineers to identify the primary issues and implement correc…