When turned on, "GitHub Copilot checks code completion suggestions with their surrounding

Then it fine-tunes the LLM to maximize the learned reward using RL techniques.

In this paper, we propose a novel adaptive preference loss, underpinned by distributionally robust optimization (DRO), designed to address this uncertainty in preference strength.

Preference learning algorithms (e.g., RLHF and DPO) are frequently used to steer LLMs to produce generations that are more preferred by humans, but our understanding of their inner workings is still limited.

Recently, large language models (LLMs) have achieved superior performance, empowering the development of large multimodal agents (LMAs).