Reinforcement learning environments
A reinforcement learning environment is a simulated task with tools, state and an automatic grader, in which a model practices and receives a reward for success.
Last reviewed
What reinforcement learning environments contains
- Task definition
- Tool and state simulation
- Automatic verifier or reward function
- Seed scenarios
Who buys it and how it is priced
| Attribute | Value |
|---|---|
| Belongs to | post-training asset |
| Main buyers | Frontier labs training agents on software, browsing and office tasks |
| Pricing | Typically priced per environment or per project, with premiums for realism, verifier quality and difficulty. |
Quality criteria and risks
Quality criteria
- Reliable verifier with few false rewards
- Resistance to reward hacking
- Task diversity
- Reproducibility
Risks
- Reward hacking
- Environments that are too easy or too narrow
- License terms on cloned software
Providers that list reinforcement learning environments
Auction and pricing mechanisms that fit
- Reverse auction: Fits a buyer that knows what it needs and wants several vendors to compete, such as sourcing expert data from labs.
- Posted-price sale: Fits catalogue datasets that can be sold many times, such as cloud data exchange listings.
Key terms
- RL environment
- An RL environment is a simulated task with tools and an automatic grader in which a model practices and earns rewards.
- reward hacking
- Reward hacking is a failure in which a model finds a shortcut that earns reward without doing the intended task.
- evaluation set
- An evaluation set is a collection of tasks used to measure a model, kept separate from training data.
Frequently asked questions
- Why are RL environments sold separately from datasets?
- An environment is executable. It produces fresh training signal on demand instead of a fixed set of examples.