Verifiers' core positioning and values
Verifiers is a library of infrastructure tools focused on reinforcement learning training for large language models (LLMs). It solves the problem of building RL training environments through modular design, and mainly contains three core functional components: a standardized environment interface that provides aSingleTurnEnv/ToolEnv/MultiTurnEnvEnvironment type, optimized based on vLLMGRPOTrainertrainers, and combinableRubric incentivesThe
- The environment module supports complete protocols from single response to multi-round interactions, allowing developers to quickly build RL environments for mathematical reasoning, tool invocation, and other scenarios
- The trainer implements the asynchronous GRPO algorithm, which significantly improves multi-GPU training efficiency through deep integration with the vLLM inference engine
- The Rubric system allows the definition of weighted scoring systems, such as combining code correctness (70%) and style specification (30%) into a composite award
The tool library significantly lowers the engineering threshold for LLM smart body development and is designed as an alternative to decentralized RL code implementation schemes.
This answer comes from the articleVerifiers: a library of reinforcement learning environment tools for training large language modelsThe
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