Bhaskarjit Sarmah

Bhaskarjit Sarmah

Head of Financial Services AI Research

Domyn

Bhaskarjit Sarmah, Head of Financial Services AI Research at Domyn, leverages over 11 years of data science expertise across diverse industries. Previously, at BlackRock, he pioneered machine learning solutions to bolster liquidity risk analytics, uncover pricing opportunities in securities lending, and develop market regime change detection systems using network science. Bhaskarjit's proficiency extends to natural language processing and computer vision, enabling him to extract insights from unstructured data and deliver actionable reports. Committed to empowering investors and fostering superior financial outcomes, he embodies a strategic fusion of data-driven innovation and domain knowledge in the world's largest asset management firm.

In this hands-on workshop, participants will learn how reinforcement learning (RL) is used to train large language model–based agents that can make sequential decisions, interact with environments, call tools autonomously, and improve performance through experience. 
 
We will cover RL fundamentals for LLM agents, extend Markov Decision Processes (MDPs) to agent settings, explore modular RL frameworks, and dive into practical implementations using OpenPipe’s Agent Reinforcement Trainer (ART). By the end, attendees will understand how to design, train, and evaluate RL-based LLM agents for real-world tasks. 

Prerequisites 

  • Familiarity with Large Language Models (LLMs) and Python 
  • Basic understanding of Reinforcement Learning concepts (policies, rewards, environments) 
  • Prior exposure to agent frameworks is helpful but not required 

 

*Note: These are tentative details and are subject to change. 

 

Read More

Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More

Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More
Book tickets View all speakers

We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.

Show details