May 20, 2026 · Guide
RL Environments vs. RLHF Data vs. Evals: What's the Difference?
RL environments, RLHF data, and evaluations are related but distinct. Here's what each one is, how they fit together, and which companies build which.
These three terms get used almost interchangeably, and the companies in the space often sell all three, which makes it worse. But they're genuinely different things, and knowing the difference helps when you're deciding what you actually need. The short version: RLHF data shapes a model's judgment, environments teach a model to act, and evals measure how good it got.
RLHF data
RLHF — reinforcement learning from human feedback — is built on human preference judgments. A person looks at two model outputs and says which is better, or rates a response against a guideline. Aggregate enough of those judgments and you can train a reward model that captures human taste, then use it to steer the model's behavior. RLHF is about alignment and quality of judgment: tone, helpfulness, safety, following instructions. It's human-centric and largely static — the judgments are collected, not generated live by an agent acting in a world.
RL environments
An environment is interactive. Instead of a human rating a finished answer, the agent is dropped into a task — a codebase, a browser, an app — takes a sequence of actions, and the environment automatically scores the outcome. The reward usually comes from whether the task objectively succeeded (did the tests pass? did the form submit correctly?) rather than from human preference. Environments are about capability: can the agent actually do the multi-step work? Many can be reset and run millions of times without a human in the loop.
Evals
An evaluation is a fixed test used to measure a model, not to train it. It might be built on top of an environment (run the agent through a standardized set of tasks and report the pass rate) or on a static dataset. The defining feature is that there's no learning loop — you're taking a measurement, often to compare model versions or vendors. A good environment frequently doubles as an eval.
How they fit together
In practice these stack. A lab might use RLHF data to shape a model's judgment, RL environments to train its task-completion ability, and evals to measure both before shipping. That's exactly why many vendors offer the whole stack — the human feedback and the environment to train against it are complementary, and the eval falls out of the environment almost for free.
Which companies build which
Some companies lead with human data and RLHF and have extended into environments; others are environment-first; a few are eval- and data-curation-focused. You can filter the full directory by what each company actually builds on our RL environment companies list.