1 citations · 1 across the 4 of their papers we have counts for
4 papers
When Actions Teach You to Think: Reasoning-Action Synergy via Reinforcement Learning in Conversational Agents
Mrinal Rawat, Arkajyoti Chakraborty, Neha Gupta +1
Supervised fine-tuning (SFT) has emerged as one of the most effective ways to improve the performance of large language models (LLMs) in downstream tasks. However, SFT can have dif…
Pre-Act: Multi-Step Planning and Reasoning Improves Acting in LLM Agents
Mrinal Rawat, Ambuje Gupta, Rushil Goomer +3
The ReAct (Reasoning + Action) capability in large language models (LLMs) has become the foundation of modern agentic systems. Recent LLMs, such as DeepSeek-R1 and OpenAI o1/o3, ex…
Controllable Discovery of Intents: Incremental Deep Clustering Using Semi-Supervised Contrastive Learning
Mrinal Rawat, Hithesh Sankararaman, Victor Barres
Deriving value from a conversational AI system depends on the capacity of a user to translate the prior knowledge into a configuration. In most cases, discovering the set of releva…
REFINE on Scarce Data: Retrieval Enhancement through Fine-Tuning via Model Fusion of Embedding Models
Ambuje Gupta, Mrinal Rawat, Andreas Stolcke +1
Retrieval augmented generation (RAG) pipelines are commonly used in tasks such as question-answering (QA), relying on retrieving relevant documents from a vector store computed usi…