How to Run an Autoresearch Workflow with RL Agent Skills and NVIDIA NeMo

Summary

Setting up reinforcement learning workflows and running repetitive experimentation often consumes significant engineering time before meaningful metrics emerge. To address this, an autonomous autoresearch workflow uses a frontier coding agent running Codex with GPT 5.5 to manage NVIDIA NeMo RL and NeMo Gym on an NVIDIA Brev GPU instance. The agent executes full-stack setup, creates a novel NeMo Gym visual counting environment, tunes Qwen3-VL-2B-Instruct accuracy from 25.0% to 96.9%, and implements an off-policy reinforcement learning algorithm directly from a research paper. Reusable skills, including brev-etiquette, session-memory, and autoresearch, enforce local conventions and prevent context drift during long executions. This setup offloads environment configuration, dependency resolution, and experiment tracking while preserving human oversight for goals and strategy.

Takeaways

1. 1

   Codex created a custom NeMo Gym visual counting task and boosted Qwen3-VL-2B-Instruct accuracy from 25.0% to 96.9%.

2. 2

   Three structured agent skills (brev-etiquette, session-memory, and autoresearch) provide durable memory and prevent filesystem scattering or context drift.

3. 3

   Codex translated an off-policy RL algorithm (OAPL) from a research paper into code, achieving higher accuracy with fewer steps than DAPO on the Qwen3-1.7B model.