Models
RL Environments
The interactive setting a model acts in during reinforcement learning.
In depth
The interactive setting a model acts in during reinforcement learning. It supplies observations, accepts the model's actions, and returns a reward plus a signal when an episode ends. OpenAI Gym standardized this reset-and-step interface in 2016, and its successor Gymnasium still defines it. For LLM agents, an environment is usually a sandboxed task, such as a repository with tests, a terminal, or a set of tools, paired with a verifier that scores the outcome.
Primary source: arxiv.org
Example
For LLM agents, an environment is usually a sandboxed task, such as a repository with tests, a terminal, or a set of tools, paired with a verifier that scores the outcome.
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FAQ
Common questions
What is RL Environments?
The interactive setting a model acts in during reinforcement learning.
Why does RL Environments matter for AI developers?
RL Environments sits in the Models part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about RL Environments?
Developers Digest publishes tutorials and videos that cover Models topics including RL Environments. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
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Rubric-Based Grading
Scoring a model's output against an explicit, written rubric that defines what each score level means for each criterion, instead of asking for an open-ended quality rating.
Models
Process Reward Model (PRM)
A reward model that scores every intermediate step of a solution, not just the final answer.
Models
Reasoning Models
Models specifically trained or prompted to show their step-by-step thinking process before producing a final answer.
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