Model Fine-Tuning & RLHF Contributor
Participated in model fine-tuning and reinforcement learning from human feedback (RLHF) processes for large language model training. Collaborated in the evaluation, rating, and optimization of model outputs to improve language generation quality. Contributed to iterative model improvement cycles focusing on AI agent systems and quant trading modules. • Directly involved in RLHF as part of end-to-end model training. • Improved output quality through systematic annotation and human feedback. • Used internal/proprietary tools alongside PyTorch and TensorFlow. • Targeted applications include AI agents and quantitative trading AI solutions.