1 citations · 1 across the 4 of their papers we have counts for
5 papers
From Codebase to Culprit (C2C): Reducing the Search Space for Bugs with Semantic Retrieval and Hierarchical Reinforcement Learning
Ankur Garg, Corey Yang-Smith, Rishav Rishav +2
We introduce C2C (From Codebase to Culprit), a framework for precise bug localization that progressively reduces the debugging search space across multiple levels of granularity: f…
ContraPrompt: Contrastive Prompt Optimization via Dyadic Reasoning Trace Analysis
Rishav Rishav, Pushpak Pujari, Pushpendre Rastogi
Prompt optimization methods either analyze individual failures in isolation or compare prompt variants across examples, operating on single execution traces with no access to the r…
Handling Delay in Real-Time Reinforcement Learning
Ivan Anokhin, Rishav Rishav, Matthew Riemer +3
Real-time reinforcement learning (RL) introduces several challenges. First, policies are constrained to a fixed number of actions per second due to hardware limitations. Second, th…
Behaviour Discovery and Attribution for Explainable Reinforcement Learning
Rishav Rishav, Somjit Nath, Vincent Michalski +1
Building trust in reinforcement learning (RL) agents requires understanding why they make certain decisions, especially in high-stakes applications like robotics, healthcare, and f…
KD-LoRA: A Hybrid Approach to Efficient Fine-Tuning with LoRA and Knowledge Distillation
Rambod Azimi, Rishav Rishav, Marek Teichmann +1
Large language models (LLMs) have demonstrated remarkable performance across various downstream tasks. However, the high computational and memory requirements of LLMs are a major b…