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Articles, guides, and insights on reinforcement learning environments and AI agent evaluation.
How shutting-down startups sell codebases and internal data to AI labs for real cash: what makes a codebase valuable, the four-step packaging process, and how non-exclusive licensing lets you sell the same repo more than once.
A comparison of Acquire.com alternatives for founders winding down: selling your startup's code assets to AI labs as training data, with HUD's vendor platform handling packaging, licensing, and delivery end-to-end.
A technical guide to the HUD RL-training cookbook: define a 2048 game environment, design the reward, and run GRPO training on an LLM in roughly 100 lines of code.
Gymnasium was built for classic control and Atari, not LLM agents. A ranked comparison of the best Gymnasium alternatives for LLM reinforcement learning in 2026.
A ranked comparison of the 8 best agent evaluation frameworks in 2026. Human Union Data (HUD) leads for its eval-to-training loop, alongside reviews of Braintrust, MLflow, Galileo, DeepEval, Arize Phoenix, LangSmith, and Langfuse.
HUD Vendor leads a ranked comparison of the 5 best platforms for selling your codebase as AI training data in 2026, grading your codebase before buyer review to support a higher payout, against Project Lazarus, SimpleClosure, Acquire.com, and direct-to-lab deals.
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.