Guides

Learn which rights and terms to discuss when licensing code, tasks, and working environments for AI training.

See how code, datasets, and new training tasks reach AI buyers, and how HUD and DataVendor help with the process.

Most startups aren't great candidates for a full-company sale on Acquire.com. This guide compares Acquire.com alternatives for selling your startup's code and IP assets to AI labs, plus full-company brokerages like Flippa, Empire Flippers, QuietLight, and Microns.

A technical guide to the HUD RL-training cookbook: wrap 2048 as an environment and train a small model to play it with GRPO in about 100 lines of Python.

Gymnasium is the framework everyone learned RL on, but LLM-native teams never adopted it for agent training. Compare HUD, verifiers / prime-rl, and TextArena as the three real Gymnasium alternatives for LLM reinforcement learning.

A ranked comparison of the 8 best agent evaluation frameworks in 2026. Human Union Data (HUD) leads for its eval-to-training loop, with reviews of Braintrust, MLflow, Galileo, DeepEval, Arize Phoenix, LangSmith, and Langfuse.

DataVendor leads a ranked comparison of the 5 best platforms for selling your codebase as AI training data in 2026. It grades your codebase before buyer review to support a higher payout, compared against Project Lazarus, SimpleClosure, Acquire.com, and direct-to-lab deals.

An end-to-end guide to the agent training pipeline: environment definition, template writing, reward design, evaluation, trajectory collection, RL training, checkpointing, and deployment.

A practical guide to Group Relative Policy Optimization. Learn how GRPO works, why it replaced PPO for verifiable-reward training, and how to run a GRPO training loop on HUD.

A practical guide to evaluating computer use agents. Six principles for high-quality CUA evals, why public benchmarks miss the mark, and how to run your first eval against a CUA benchmark like SheetBench-50.

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 for closing the eval-to-train loop. Compare HUD, Prime Intellect, Harbor, RLlib, Gymnasium, CleanRL, and in-house builds on trajectory capture, reward design, and training 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 reinforcement learning tools in 2026. Compare HUD, Harbor, RLlib, Gymnasium, Farama Foundation, and CleanRL for agent training, evaluation, and environments.

A comprehensive guide to the best reinforcement learning environment tools in 2026. Compare HUD, Gymnasium, RLlib, CleanRL, and Prime Intellect across standardization, reproducibility, benchmarking, and more.