“With this approach, autonomous AI agents translate high-level goals into hypotheses, edit and test a real codebase, keep metric-improving ideas, and hand results back to the human researcher.”

Tanya Lenz

“In the examples provided in this post, Codex first brings up a full NeMo RL and NeMo Gym stack for a vision language model (VLM) RL training smoke test. It then conceptualizes and creates a novel NeMo Gym visual counting environment from scratch, and trains the Qwen3-VL-2B-Instruct model, increasing its accuracy from 25.0% to 96.9% on the task.”

Tanya Lenz

“The goal of autoresearch is not to remove the researcher from the loop, but instead to hand off the repetitive setup and iteration work to the agent. Researchers are still responsible for setting goals, reviewing milestones, steering strategy, and making the final decision.”

Tanya Lenz

“Context drift: Long sessions can pass through multiple context compaction cycles. Without durable memory, the agent may forget the objective, loaded skills, or stop rules.”

Tanya Lenz

“Set explicit budget constraints before starting a long-running autoresearch session, such as a time limit, GPU-hour budget, or maximum number of experiments. This keeps the agent focused on the highest-value hypotheses and prevents overly broad experiment sweeps.”

Tanya Lenz