How to Run RL Autoresearch with Agent Skills | Nemotron Labs

Item: How to Run RL Autoresearch with Agent Skills | Nemotron Labs
Rating: 4
Author: PostSingularity Institute

by NVIDIA Developer

Applied Sciences & Engineering AI — Models & Research UYQArtificial intelligence (AI)UYQMMachine learning

🎙 NVIDIA Developer 👥 222K 📅 July 15, 2026 ⏱ 50 min 👁 4K 📄 tutorial 🧭 2026-08-13

Available in: English (current) Français

Keywords

RLautoresearchagent skillsNeMo RLNeMo Gym

Summary

This tutorial livestream from NVIDIA Developer demonstrates how to run reinforcement learning (RL) autoresearch using coding agents and NVIDIA’s NeMo RL and NeMo Gym libraries. The host, Shashank, walks through setting up a GPU instance on NVIDIA Brev, connecting a coding agent like Codex, and using agent skills to structure the experiment loop. The demo involves training a small vision-language model to count stars in images, starting with a GRPO smoke test, then creating a custom environment in NeMo Gym, and running an autoresearch campaign. The agent autonomously handles setup, resolves dependencies, and iterates on training recipes. After GRPO achieves only 48.5% accuracy, the agent pivots to SFT, reaching over 91% accuracy within a 5-hour budget. The video also covers best practices like session memory, approval modes, and independent evaluation to avoid self-validating loops. The discussion includes questions about safety, non-ML applications, and preventing epistemic loops, with answers emphasizing external benchmarks and separate evaluators.

154 words

Critical Evaluation

Value of the Information & Strength of the Argument

The video provides valuable practical insights into automating RL research, demonstrating a concrete workflow with real results. The argumentation is solid, based on a live demo and personal experience, though it is promotional and lacks formal scientific rigor. The value lies in the actionable steps and the demonstration of agent skills improving efficiency.

Scientific Rigor, Source Quality, Title Accuracy

The scientific rigor is moderate; the video is a tutorial rather than a peer-reviewed study. Sources are primarily NVIDIA’s own tools and platforms, with no external citations. The title accurately reflects the content, and the presentation is clear and well-structured. No comments were provided for analysis.

114 words

Title / Content Match

The title accurately reflects the content: a tutorial on running RL autoresearch using agent skills, with a focus on NeMo RL and NeMo Gym.

Quality & Reliability

8/10

The video is a practical tutorial from NVIDIA Developer, demonstrating a real workflow with reproducible steps. It includes live demos and references to official tools (NeMo RL, NeMo Gym, Brev). However, it is promotional in nature and lacks formal citations or peer-reviewed sources.

Key Moments

Markers derived by PSI from the transcript: the creator did not define chapters.

Cited Sources

NVIDIA Brev — Platform for renting GPU instances used in the demo
NeMo RL — NVIDIA's reinforcement learning library
NeMo Gym — NVIDIA's library for creating and evaluating RL environments

Concurring Sources

NeMo RL Documentation — Official documentation for NeMo RL, supporting the workflow shown.

Contribution & Novelties

The video demonstrates a novel approach to automating RL research using coding agents and structured agent skills, showing how to run end-to-end experiments on a single GPU. It highlights the importance of session memory and structured workflows for long-running tasks.

Pour aller plus loin :

Reinforcement Learning — Foundational concepts.
GRPO — Group Relative Policy Optimization, a key algorithm mentioned.
NeMo Framework — NVIDIA’s official documentation.

66 words

Radar Profile

The radar profile shows high scores in information quantity and reliability, with moderate technical depth. The video is strong on practical guidance but less on theoretical depth, reflecting its tutorial nature.

Reliability 8/10