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Mastering Agentic Techniques: AI Agent Reinforcement Learning

Source: developer.nvidia.com
Author: Elizabeth Goodman
Date: Published Jul 1, 2026

Context

Reinforcement learning becomes useful when an 's success can be scored reliably. NVIDIA's training guide describes a loop that generates attempts, checks their outcomes, and updates model weights to favor better behavior. For , valid formatting is only one part of success: the selected tool, its arguments, and the execution outcome also matter. The guide recommends testing the scoring function against sample outputs before training and evaluating the resulting model on separate tasks it never trained on. This makes the evaluator a critical dependency: a rising reward can otherwise reflect learning the scoring shortcut rather than completing the intended task.

Terms in this piece · Glossary

AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
tool use — A model's ability to call external functions — run code, search the web, edit files — instead of only generating text.
RLHF — Reinforcement learning from human feedback — training a model to prefer answers humans rate as better, which turns a raw text predictor into a usable assistant.

Key quotes

“NVIDIA Nemotron 3 Super was post-trained using multi-environment RL across 21 NVIDIA NeMo Gym verifiers and 37 datasets, generating about 1.2 million environment rollouts.”
Sylendran Arunagiri

“RL works best when the model can sometimes produce the right behavior but doesn’t do so reliably. If the reward is wrong, RL will optimize the wrong behavior.”
Sylendran Arunagiri

“Too much shaping can teach the model to optimize the checklist instead of the task.”
Sylendran Arunagiri

“Before training, run your reward function against 50-100 model outputs and inspect the scores manually. If the reward disagrees with your judgment, fix the reward.”
Sylendran Arunagiri

“Evals and environments are two sides of the same system. A good eval conveys whether the model succeeded, and a good RL environment turns that signal into training data.”
Sylendran Arunagiri

Read the source developer.nvidia.com