LLM-as-a-Verifier
What it is
A Python library that uses LLMs to provide fine-grained verification feedback for agent trajectories. It leverages logprob distributions and criteria decomposition to score candidates for best-of-N selection, progress tracking, and reinforcement learning.
The problem it solves
Standard LLM evaluation often lacks the granularity needed to distinguish between subtly different agent trajectories or to provide actionable feedback for test-time scaling and RL.
What you could use it for
- Selecting the best agent trajectory from a pool of rollouts
- Tracking agent progress step-by-step during execution
- Providing fine-grained rewards for reinforcement learning
- Evaluating coding, robotics, and medical agent performance
Read the source ↗Back to the library →
Summary written by an AI model from the source; check the source before you rely on it.