This system paper describes our conversational AI agent developed for the SCI-CHAT competition. The goal is to build automated dialogue agents that can have natural, coherent conversations with humans over multiple turns. Our model is based on fine-tuning the Snorkel-Mistral-PairRM-DPO language model on podcast conversation transcripts. This allows the model to leverage Snorkel-Mistral-PairRMDPO’s linguistic knowledge while adapting it for multi-turn dialogue modeling using LoRA. During evaluation, human judges will converse with the agent on specified topics and provide ratings on response quality. Our system aims to demonstrate how large pretrained language models, when properly adapted and evaluated, can effectively converse on open-ended topics spanning multiple turns.