LLM-as-a-Verifier

Test-time scaling is effective for agentic tasks, but picking the winner among many candidates is the bottleneck. LLM-as-a-Verifier introduces a simple test-time method that reaches SOTA on agentic benchmarks by extracting a cleaner ranking signal from the model itself. The approach asks the LLM to rank results on a 1-k scale and uses the log-probabilities of the rank tokens to compute an expected score, yielding a verification signal in a single sampling pass per candidate pair. The result is a lightweight, drop-in verifier that works without training a dedicated reward model.