ALPHALAB: Autonomous Multi-Agent Research Across Optimization Domains with Frontier LLMs

We built AlphaLab to automate quantitative research at Morgan Stanley’s Machine Learning Research Lab - the experimental grind of architecture search, hyperparameter tuning, and literature review that consumes most of a researcher's time. To show it generalizes, we ran it on three deliberately different domains: CUDA kernel optimization (4.4× mean speedup over torch.compile, 91× peak), LLM pretraining (22% lower validation loss under a 20-minute budget), and traffic forecasting (23–25% RMSE improvement after the system independently found and tuned TFT and iTransformer from the literature). AlphaLab is an agentic harness that takes a dataset and a natural-language objective and runs a full research campaign across three phases: it explores the data and surveys prior work, it constructs and adversarially validates its own evaluation framework, and then it runs experiments at scale on a multi-GPU cluster via a Strategist/Worker loop with a persistent playbook that accumulates domain knowledge across experiments. In Phase 3 - the dispatcher keeps a large cluster fully utilized indefinitely with no human in the loop, and the playbook ends up containing domain-specific methodology that didn't exist anywhere in the prompts at launch. This talk walks through the three phases, what we learned from running campaigns with different models, what we have learned from using this in real systems, and future areas we are exploring.