Swattik Chakrabarty

As large language models move from prototypes into enterprise workflows, teams across the industry increasingly face a practical operational alignment problem: how to steer model behavior toward business outcomes in a reliable and measurable way. At the same time for many practitioners a critical decision remains unclear: whether a use case is best addressed through prompt engineering, supervised fine-tuning, or reinforcement learning. This talk introduces a structured framework for choosing the right approach and a practical method for translating business objectives into reward signals that can be optimized, evaluated, and audited. Through hands on experiments in incremental preference learning using reward modeling and policy optimization, the session demonstrates how meaningful behavioral shifts can be achieved even with small, carefully curated datasets. It also examines the stability trade-offs between staged training and online RL updates. Additionally, the session distills practical guidance on designing verifiable reward signals from ambiguous business objectives. This segment identifies when exploration and delayed rewards make reinforcement learning necessary and how to avoid common failure modes, such as reward hacking and instability. Rather than focusing on scale alone, the session emphasizes disciplined reward design and systematic experimentation as the foundation for deploying reinforcement learning effectively in enterprise LLM systems.