PreferenceModel Reviews — Discover what people think of this product.

Karotte is an open-source framework for constructing hardened reinforcement learning environments designed to resist reward hacking and agent jailbreaks. Built and battle-tested through over a million internal evaluation runs and controlled red-teaming, Karotte focuses on making it easier to create secure, alignment-focused RL setups for advanced models.

By providing opinionated defaults and safety-first primitives, Karotte helps researchers and engineers avoid subtle failure modes where agents exploit loopholes instead of solving the intended task. From CSV processing tasks to environments that compile and execute model-generated code, Karotte is designed to defend against exploits such as fork bombs, grader monkeypatching, memory exhaustion, and background process abuse.

Key advantages include:

• Hardened environment primitives for alignment-sensitive RL experiments
• Built-in defenses against reward hacking and sandbox escape attempts
• Support for complex code-execution tasks with secure grading workflows
• A simple CLI (uvx karotte create-env my-env) to bootstrap new environments quickly

Karotte is ideal for teams training frontier models, safety researchers probing misalignment risks, and developers building high-stakes RL benchmarks where environment integrity and correct reward signals are critical.