Articles, guides, and insights on reinforcement learning environments and AI agent evaluation.
An end-to-end walkthrough of the seven-stage agent training pipeline: environment definition, scenarios, reward design, evaluation, RL training, checkpointing, and deployment — using HUD as the running example.
A practical guide to Group Relative Policy Optimization: how the algorithm works, how it differs from PPO and RLHF, how to design reward functions for it, and how to run a GRPO training run on HUD.
How computer use agent evaluation differs from chatbot evaluation, the six principles of a high-quality CUA eval, and how to run your first eval on SheetBench-50 in three commands.
A practical introduction to RL environments, the core components that make them work, and a step-by-step example of building one in Python using the HUD SDK.
A ranked comparison of the best platforms for publishing RL environments to model labs. Evaluates HUD, Harbor, Prime Intellect, Gymnasium, and RLlib on discoverability, execution, scoring, deployment, and documentation.
A step-by-step breakdown of Analyst Arena: an agent-vs-agent trading simulator where a HUD-trained model outperformed GPT 5.2 through better training infrastructure, tool design, and evaluation iteration.
A comparison of seven platforms that close the gap between agent evaluation and RL training. Covers trajectory capture, reward design, environment reuse, and training-path readiness.
A practical guide to building scoring systems for RL environments. Learn how to design verifiers, pass/fail checks, rubrics, and reward functions that produce reliable training signals.
A ranked guide to the best RL tools for agent training. Compare HUD, Harbor, RLlib, Gymnasium, Farama Foundation, and CleanRL across environment realism, evaluation design, scaling, and observability.
A comprehensive guide to the best RL environment tools in 2026, evaluated against standardization, reproducibility, benchmarking, accessibility, extensibility, and training loop support.